An Overview of Aggregating Vertical Results into Web Search Results
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paper_title: ProFusion: Intelligent Fusion from Multiple, Distributed Search Engines

paper_content:

The explosive growth of the World Wide Web, and the resulting information overload, has led to a mini-explosion in World Wide Web search engines. This mini-explosion, in turn, led to the development of ProFusion, a meta search engine. Educators, like other users, do not have the time to evaluate multiple search engines to knowledgeably select the best for their uses. Nor do they have the time to submit each query to multiple search engines and wade through the resulting flood of good information, duplicated information, irrelevant information, and missing documents. ProFusion sends user queries to multiple underlying search engines in parallel, retrieves and merges the resulting URLs. It identifies and removes duplicates and creates one relevance-ranked list. If desired, the actual documents can be pre-fetched to remove yet more duplicates and broken links. ProFusion's performance has been compared to the individual search engines and other meta searchers, demonstrating its ability to retrieve more relevant information and present fewer duplicates pages. The system can automatically analyze queries to identify its topic(s) and, based on that analysis, select the most appropriate search engines for the query.
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paper_title: The MetaCrawler Architecture for Resource Aggregation on the Web

paper_content:

The paper discusses the MetaCrawler Softbot parallel Web search service that has been available at the University of Washington since June 1995. It provides users with a single interface for querying popular general-purpose Web search services, such as Lycos and AltaVista, and has some sophisticated features that allow it to obtain results of much higher quality than simply regurgitating the output from each search service.
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paper_title: AllInOneNews: development and evaluation of a large-scale news metasearch engine

paper_content:

AllInOneNews is the largest news metasearch engine in the world, connecting to over 1,000 news sites over 150 countries. Implementing a large-scale metasearch engine like AllInOneNews needs to overcome unique challenges not faced by building small metasearch engines such as developing highly scalable search engine selection techniques. In this paper, we discuss these unique challenges and our solutions to these challenges. We also discuss some novel features of AllInOneNews such as highly automated solution and semantic query match. This paper also reports the results of a comparative evaluation of three commercial news search systems, one search engine - Google News and two metasearch engines - Mamma News and AllInOneNews. Several measures such as effectiveness, diversity and time-sensitivity are used to perform the comparison. Another contribution of this paper is that we introduce a novel scheme to compare multiple news search systems in a combined measure that takes both relevance and time-sensitivity of retrieved information into consideration.
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paper_title: Advanced Metasearch Engine Technology

paper_content:

Among the search tools currently on the Web, search engines are the most well known thanks to the popularity of major search engines such as Google and Yahoo!. While extremely successful, these major search engines do have serious limitations. This book introduces large-scale metasearch engine technology, which has the potential to overcome the limitations of the major search engines. Essentially, a metasearch engine is a search system that supports unified access to multiple existing search engines by passing the queries it receives to its component search engines and aggregating the returned results into a single ranked list. A large-scale metasearch engine has thousands or more component search engines. While metasearch engines were initially motivated by their ability to combine the search coverage of multiple search engines, there are also other benefits such as the potential to obtain better and fresher results and to reach the Deep Web. The following major components of large-s ale metasearch engines will be discussed in detail in this book: search engine selection, search engine incorporation, and result merging. Highly scalable and automated solutions for these components are emphasized. The authors make a strong case for the viability of the large-scale metasearch engine technology as a competitive technology for Web search. Table of Contents: Introduction / Metasearch Engine Architecture / Search Engine Selection / Search Engine Incorporation / Result Merging / Summary and Future Research
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paper_title: Architecture of a metasearch engine that supports user information needs

paper_content:

When a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Users with specific needs, such as “research papers” or “homepages,” are not able to express these needs in a way that affects the decisions made by the metasearch engine. In this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. Users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. Our architecture combines several powerful approaches together in a single general purpose metasearch engine.
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paper_title: Building efficient and effective metasearch engines

paper_content:

Frequently a user's information needs are stored in the databases of multiple search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. To support unified access to multiple search engines, a metasearch engine can be constructed. When a metasearch engine receives a query from a user, it invokes the underlying search engines to retrieve useful information for the user. Metasearch engines have other benefits as a search tool such as increasing the search coverage of the Web and improving the scalability of the search. In this article, we survey techniques that have been proposed to tackle several underlying challenges for building a good metasearch engine. Among the main challenges, the database selection problem is to identify search engines that are likely to return useful documents to a given query. The document selection problem is to determine what documents to retrieve from each identified search engine. The result merging problem is to combine the documents returned from multiple search engines. We will also point out some problems that need to be further researched.
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paper_title: Intelligent metasearch engine for knowledge management

paper_content:

The explosive growth of available information sources and the resulting information overload pose several problems for users in many business organizations and educational institutions. First, searching through several information sources, one at a time, is a source of enormous frustration for users. Second, top-ranked documents in search results are frequently irrelevant to what users are interested in. To address these problems, we have developed ixmeta™, a powerful metasearch engine that gathers, evaluates, ranks, and reports the most relevant results from multiple information sources, including library catalogs, proprietary databases, intranets, and Web search engines. In addition to basic metasearch capabilities, ixmetafind uses personalization and clustering techniques to find the most relevant results for users. In this paper, we briefly describe technologies used in ixmetafind and present pinpoint™ from Sagebrush Corporation, the smart research tool™ in the kindergarten through twelfth grade (K-12) school environment. Pinpoint showcases ixmetafind in the knowledge management domain of the K-12 school environment.
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paper_title: Full-text federated search in peer-to-peer networks

paper_content:

Peer-to-peer (P2P) networks integrate autonomous computing resources without requiring a central coordinating authority, which makes them a potentially robust and scalable model for providing federated search capability to large-scale networks of text digital libraries. However, P2P networks have so far mostly used simple search techniques based on document names or controlled-vocabulary terms, and provided very limited support for full-text search of document contents.
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paper_title: Effective collection metasearch in a hierarchical environment: global vs. localized retrieval performance

paper_content:

We compare standard global IR searching with user-centric localized techniques to address the database selection problem. We conduct a series of experiments to compare the retrieval effectiveness of three separate search modes applied to a hierarchically structured data environment of textual database representations. The data environment is represented as a tree-like directory containing over 15,000 unique databases and over 100,000 total leaf nodes. Our search modes consist of varying degrees of browse and search, from a global search at the root node to a refined search at a sub-node using dynamically-calculated inverse document frequencies (idfs) to score candidate databases for probable relevance. Our findings indicate that a browse and search approach that relies upon localized searching from sub-nodes is capable of producing the most effective results.
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paper_title: Experiences with selecting search engines using metasearch

paper_content:

Search engines are among the most useful and high-profile resources on the Internet. The problem of finding information on the Internet has been replaced with the problem of knowing where search engines are, what they are designed to retrieve, and how to use them. This article describes and evaluates SavvySearch, a metasearch engine designed to intelligently select and interface with multiple remote search engines. The primary metasearch issue examined is the importance of carefully selecting and ranking remote search engines for user queries. We studied the efficacy of SavvySearch's incrementally acquired metaindex approach to selecting search engines by analyzing the effect of time and experience on performance. We also compared the metaindex approach to the simpler categorical approach and showed how much experience is required to surpass the simple scheme.
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paper_title: Query-based sampling of text databases

paper_content:

The proliferation of searchable text databases on corporate networks and the Internet causes a database selection problem for many people. Algorithms such as gGLOSS and CORI can automatically select which text databases to search for a given information need, but only if given a set of resource descriptions that accurately represent the contents of each database. The existing techniques for a acquiring resource descriptions have significant limitations when used in wide-area networks controlled by many parties. This paper presents query-based sampling , a new technicque for acquiring accurate resource descriptions. Query-based sampling does not require the cooperation of resource providers, nor does it require that resource providers use a particular search engine or representation technique. An extensive set of experimental results demonstrates that accurate resource descriptions are crated, that computation and communication costs are reasonable, and that the resource descriptions do in fact enable accurate automatic dtabase selection.
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paper_title: Efficient search engine measurements

paper_content:

We address the problem of measuring global quality met-rics of search engines, like corpus size, index freshness, anddensity of duplicates in the corpus. The recently proposedestimators for such metrics [2, 6] suffer from significant biasand/or poor performance, due to inaccurate approximationof the so called .document degrees..We present two new estimators that are able to overcomethe bias introduced by approximate degrees. Our estimatorsare based on a careful implementation of an approximateimportance sampling procedure. Comprehensive theoreti-cal and empirical analysis of the estimators demonstratesthat they have essentially no bias even in situations wheredocument degrees are poorly approximated.Building on an idea from [6], we discuss Rao Blackwelliza-tion as a generic method for reducing variance in searchengine estimators. We show that Rao-Blackwellizing ourestimators results in significant performance improvements,while not compromising accuracy.
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paper_title: Early user---system interaction for database selection in massive domain-specific online environments

paper_content:

The continued growth of very large data environments such as Westlaw and Dialog, in addition to the World Wide Web, increases the importance of effective and efficient database selection and searching. Current research focuses largely on completely autonomous and automatic selection, searching, and results merging in distributed environments. This fully automatic approach has significant deficiencies, including reliance upon thresholds below which databases with relevant documents are not searched (compromised recall). It also merges documents, often from disparate data sources that users may have discarded before their source selection task proceeded (diluted precision). We examine the impact that early user interaction can have on the process of database selection. After analyzing thousands of real user queries, we show that precision can be significantly increased when queries are categorized by the users themselves, then handled effectively by the system. Such query categorization strategies may eliminate limitations of fully automated query processing approaches. Our system harnesses the WIN search engine, a sibling to INQUERY, run against one or more authority sources when search is required. We compare our approach to one that does not recognize or utilize distinct features associated with user queries. We show that by avoiding a one-size-fits-all approach that restricts the role users can play in information discovery, database selection effectiveness can be appreciably improved.
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paper_title: Integration of news content into web results

paper_content:

Aggregated search refers to the integration of content from specialized corpora or verticals into web search results. Aggregation improves search when the user has vertical intent but may not be aware of or desire vertical search. In this paper, we address the issue of integrating search results from a news vertical into web search results. News is particularly challenging because, given a query, the appropriate decision---to integrate news content or not---changes with time. Our system adapts to news intent in two ways. First, by inspecting the dynamics of the news collection and query volume, we can track development of and interest in topics. Second, by using click feedback, we can quickly recover from system errors. We define several click-based metrics which allow a system to be monitored and tuned without annotator effort.
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paper_title: Sources of evidence for vertical selection

paper_content:

Web search providers often include search services for domain-specific subcollections, called verticals, such as news, images, videos, job postings, company summaries, and artist profiles. We address the problem of vertical selection, predicting relevant verticals (if any) for queries issued to the search engine's main web search page. In contrast to prior query classification and resource selection tasks, vertical selection is associated with unique resources that can inform the classification decision. We focus on three sources of evidence: (1) the query string, from which features are derived independent of external resources, (2) logs of queries previously issued directly to the vertical, and (3) corpora representative of vertical content. We focus on 18 different verticals, which differ in terms of semantics, media type, size, and level of query traffic. We compare our method to prior work in federated search and retrieval effectiveness prediction. An in-depth error analysis reveals unique challenges across different verticals and provides insight into vertical selection for future work.
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paper_title: Vertical selection in the presence of unlabeled verticals

paper_content:

Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive cross-vertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals
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paper_title: Federated search of text search engines in uncooperative environments

paper_content:

Conventional search engines like Google provide access to Web information that can be acquired easily by crawling hyperlinks. However, a large amount of information cannot be copied arbitrarily by conventional search engines. This type of hidden information, which is very valuable, can only be accessed via an alternative search model other than the centralized retrieval model used by conventional search engines.
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paper_title: Relevant document distribution estimation method for resource selection

paper_content:

Prior research under a variety of conditions has shown the CORI algorithm to be one of the most effective resource selection algorithms, but the range of database sizes studied was not large. This paper shows that the CORI algorithm does not do well in environments with a mix of "small" and "very large" databases. A new resource selection algorithm is proposed that uses information about database sizes as well as database contents. We also show how to acquire database size estimates in uncooperative environments as an extension of the query-based sampling used to acquire resource descriptions. Experiments demonstrate that the database size estimates are more accurate for large databases than estimates produced by a competing method; the new resource ranking algorithm is always at least as effective as the CORI algorithm; and the new algorithm results in better document rankings than the CORI algorithm.
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paper_title: Query-based sampling of text databases

paper_content:

The proliferation of searchable text databases on corporate networks and the Internet causes a database selection problem for many people. Algorithms such as gGLOSS and CORI can automatically select which text databases to search for a given information need, but only if given a set of resource descriptions that accurately represent the contents of each database. The existing techniques for a acquiring resource descriptions have significant limitations when used in wide-area networks controlled by many parties. This paper presents query-based sampling , a new technicque for acquiring accurate resource descriptions. Query-based sampling does not require the cooperation of resource providers, nor does it require that resource providers use a particular search engine or representation technique. An extensive set of experimental results demonstrates that accurate resource descriptions are crated, that computation and communication costs are reasonable, and that the resource descriptions do in fact enable accurate automatic dtabase selection.
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paper_title: Using query logs to establish vocabularies in distributed information retrieval

paper_content:

Users of search engines express their needs as queries, typically consisting of a small number of terms. The resulting seacch engine query logs are valuable resources that can be used to predict how people interact with the search system. In this paper, we introduce two novel applications of query logs, in the context of distributed information retrieval. First, we use query log terms to guide sampling from uncooperative distributed collections. We show that while our sampling strategy is at least as efficient as current methods, it consistently performs better. Second, we propose and evaluate a pruning strategy that uses query log information to eliminate terms. Our experiments show that our proposed pruning method maintains the accuracy achieved by complete indexes, while decreasing the index size by up to 60%. While such pruning may not always be desirable in practice, it provides a useful benchmark against which other pruning strategies can be measured.
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paper_title: Distributed Search over the Hidden Web: Hierarchical Database Sampling and Selection

paper_content:

Many valuable text databases on the web have non-crawlable contents that are "hidden" behind search interfaces. Metasearchers are helpful tools for searching over many such databases at once through a unified query interface. A critical task for a metasearcher to process a query efficiently and effectively is the selection of the most promising databases for the query, a task that typically relies on statistical summaries of the database contents. Unfortunately, web-accessible text databases do not generally export content summaries. In this paper, we present an algorithm to derive content summaries from "uncooperative" databases by using "focused query probes," which adaptively zoom in on and extract documents that are representative of the topic coverage of the databases. Our content summaries are the first to include absolute document frequency estimates for the database words. We also present a novel database selection algorithm that exploits both the extracted content summaries and a hierarchical classification of the databases, automatically derived during probing, to compensate for potentially incomplete content summaries. Finally, we evaluate our techniques thoroughly using a variety of databases, including 50 real web-accessible text databases. Our experiments indicate that our new content-summary construction technique is efficient and produces more accurate summaries than those from previously proposed strategies. Also, our hierarchical database selection algorithm exhibits significantly higher precision than its flat counterparts.
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paper_title: Federated search of text search engines in uncooperative environments

paper_content:

Conventional search engines like Google provide access to Web information that can be acquired easily by crawling hyperlinks. However, a large amount of information cannot be copied arbitrarily by conventional search engines. This type of hidden information, which is very valuable, can only be accessed via an alternative search model other than the centralized retrieval model used by conventional search engines.
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paper_title: Unified utility maximization framework for resource selection

paper_content:

This paper presents a unified utility framework for resource selection of distributed text information retrieval. This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text databases. With the estimated relevance information, resource selection can be made by explicitly optimizing the goals of different applications. Specifically, when used for database recommendation, the selection is optimized for the goal of high-recall (include as many relevant documents as possible in the selected databases); when used for distributed document retrieval, the selection targets the high-precision goal (high precision in the final merged list of documents). This new model provides a more solid framework for distributed information retrieval. Empirical studies show that it is at least as effective as other state-of-the-art algorithms.
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paper_title: Relevant document distribution estimation method for resource selection

paper_content:

Prior research under a variety of conditions has shown the CORI algorithm to be one of the most effective resource selection algorithms, but the range of database sizes studied was not large. This paper shows that the CORI algorithm does not do well in environments with a mix of "small" and "very large" databases. A new resource selection algorithm is proposed that uses information about database sizes as well as database contents. We also show how to acquire database size estimates in uncooperative environments as an extension of the query-based sampling used to acquire resource descriptions. Experiments demonstrate that the database size estimates are more accurate for large databases than estimates produced by a competing method; the new resource ranking algorithm is always at least as effective as the CORI algorithm; and the new algorithm results in better document rankings than the CORI algorithm.
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paper_title: The effectiveness of GIOSS for the text database discovery problem

paper_content:

The popularity of on-line document databases has led to a new problem: finding which text databases (out of many candidate choices) are the most relevant to a user. Identifying the relevant databases for a given query is the text database discovery problem . The first part of this paper presents a practical solution based on estimating the result size of a query and a database. The method is termed GlOSS—Glossary of Servers Server . The second part of this paper evaluates the effectiveness of GlOSS based on a trace of real user queries. In addition, we analyze the storage cost of our approach.
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paper_title: Query-based sampling of text databases

paper_content:

The proliferation of searchable text databases on corporate networks and the Internet causes a database selection problem for many people. Algorithms such as gGLOSS and CORI can automatically select which text databases to search for a given information need, but only if given a set of resource descriptions that accurately represent the contents of each database. The existing techniques for a acquiring resource descriptions have significant limitations when used in wide-area networks controlled by many parties. This paper presents query-based sampling , a new technicque for acquiring accurate resource descriptions. Query-based sampling does not require the cooperation of resource providers, nor does it require that resource providers use a particular search engine or representation technique. An extensive set of experimental results demonstrates that accurate resource descriptions are crated, that computation and communication costs are reasonable, and that the resource descriptions do in fact enable accurate automatic dtabase selection.
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paper_title: GlOSS: text-source discovery over the Internet

paper_content:

The dramatic growth of the Internet has created a new problem for users: location of the relevant sources of documents. This article presents a framework for (and experimentally analyzes a solution to) this problem, which we call the text-source discovery problem . Our approach consists of two phases. First, each text source exports its contents to a centralized service. Second, users present queries to the service, which returns an ordered list of promising text sources. This article describes GlOSS , Glossary of Servers Server, with two versions: bGlOSS , which provides a Boolean query retrieval model, and vGlOSS , which provides a vector-space retrieval model. We also present hGlOSS , which provides a decentralized version of the system. We extensively describe the methodology for measuring the retrieval effectiveness of these systems and provide experimental evidence, based on actual data, that all three systems are highly effective in determining promising text sources for a given query.
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paper_title: Improving automatic query classification via semi-supervised learning

paper_content:

Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose Web search systems. Such classification becomes critical if the system is to return results not just from a general Web collection but from topic-specific back-end databases as well. Maintaining sufficient classification recall is very difficult as Web queries are typically short, yielding few features per query. This feature sparseness coupled with the high query volumes typical for a large-scale search service makes manual and supervised learning approaches alone insufficient. We use an application of computational linguistics to develop an approach for mining the vast amount of unlabeled data in Web query logs to improve automatic topical Web query classification. We show that our approach in combination with manual matching and supervised learning allows us to classify a substantially larger proportion of queries than any single technique. We examine the performance of each approach on a real Web query stream and show that our combined method accurately classifies 46% of queries, outperforming the recall of best single approach by nearly 20%, with a 7% improvement in overall effectiveness.
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paper_title: Building bridges for web query classification

paper_content:

Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.
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paper_title: Automatic classification of Web queries using very large unlabeled query logs

paper_content:

Accurate topical classification of user queries allows for increased effectiveness and efficiency in general-purpose Web search systems. Such classification becomes critical if the system must route queries to a subset of topic-specific and resource-constrained back-end databases. Successful query classification poses a challenging problem, as Web queries are short, thus providing few features. This feature sparseness, coupled with the constantly changing distribution and vocabulary of queries, hinders traditional text classification. We attack this problem by combining multiple classifiers, including exact lookup and partial matching in databases of manually classified frequent queries, linear models trained by supervised learning, and a novel approach based on mining selectional preferences from a large unlabeled query log. Our approach classifies queries without using external sources of information, such as online Web directories or the contents of retrieved pages, making it viable for use in demanding operational environments, such as large-scale Web search services. We evaluate our approach using a large sample of queries from an operational Web search engine and show that our combined method increases recall by nearly 40p over the best single method while maintaining adequate precision. Additionally, we compare our results to those from the 2005 KDD Cup and find that we perform competitively despite our operational restrictions. This suggests it is possible to topically classify a significant portion of the query stream without requiring external sources of information, allowing for deployment in operationally restricted environments.
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paper_title: Factors affecting click-through behavior in aggregated search interfaces

paper_content:

An aggregated search interface is designed to integrate search results from different sources (web, image, video, blog, etc) into a single result page. This paper presents two user studies investigating factors affecting users click-through behavior on aggregated search interfaces. We tested two aggregated search interfaces: one where results from the different sources are blended into a single list (called blended), and another, where results from each source are presented in a separate panel (called non-blended). A total of 1,296 search sessions performed by 48 participants were analysed in our study. Our results suggest that 1) the position of search results is significant only in the blended and not in the non-blended design; 2) participants' click-through behavior on videos is different from other sources; and finally 3) capturing a task's orientation towards particular sources is an important factor for further investigation and research.
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paper_title: Q2C@UST: our winning solution to query classification in KDDCUP 2005

paper_content:

In this paper, we describe our ensemble-search based approach, Q2C@UST (http://webprojectl.cs.ust.hk/q2c/), for the query classification task for the KDDCUP 2005. There are two aspects to the key difficulties of this problem: one is that the meaning of the queries and the semantics of the predefined categories are hard to determine. The other is that there are no training data for this classification problem. We apply a two-phase framework to tackle the above difficulties. Phase I corresponds to the training phase of machine learning research and phase II corresponds to testing phase. In phase I, two kinds of classifiers are developed as the base classifiers. One is synonym-based and the other is statistics based. Phase II consists of two stages. In the first stage, the queries are enriched such that for each query, its related Web pages together with their category information are collected through the use of search engines. In the second stage, the enriched queries are classified through the base classifiers trained in phase I. Based on the classification results obtained by the base classifiers, two ensemble classifiers based on two different strategies are proposed. The experimental results on the validation dataset help confirm our conjectures on the performance of the Q2C@UST system. In addition, the evaluation results given by the KDDCUP 2005 organizer confirm the effectiveness of our proposed approaches. The best F1 value of our two solutions is 9.6% higher than the best of all other participants' solutions. The average F1 value of our two submitted solutions is 94.4% higher than the average F1 value from all other submitted solutions.
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paper_title: Integration of news content into web results

paper_content:

Aggregated search refers to the integration of content from specialized corpora or verticals into web search results. Aggregation improves search when the user has vertical intent but may not be aware of or desire vertical search. In this paper, we address the issue of integrating search results from a news vertical into web search results. News is particularly challenging because, given a query, the appropriate decision---to integrate news content or not---changes with time. Our system adapts to news intent in two ways. First, by inspecting the dynamics of the news collection and query volume, we can track development of and interest in topics. Second, by using click feedback, we can quickly recover from system errors. We define several click-based metrics which allow a system to be monitored and tuned without annotator effort.
---
paper_title: Sources of evidence for vertical selection

paper_content:

Web search providers often include search services for domain-specific subcollections, called verticals, such as news, images, videos, job postings, company summaries, and artist profiles. We address the problem of vertical selection, predicting relevant verticals (if any) for queries issued to the search engine's main web search page. In contrast to prior query classification and resource selection tasks, vertical selection is associated with unique resources that can inform the classification decision. We focus on three sources of evidence: (1) the query string, from which features are derived independent of external resources, (2) logs of queries previously issued directly to the vertical, and (3) corpora representative of vertical content. We focus on 18 different verticals, which differ in terms of semantics, media type, size, and level of query traffic. We compare our method to prior work in federated search and retrieval effectiveness prediction. An in-depth error analysis reveals unique challenges across different verticals and provides insight into vertical selection for future work.
---
paper_title: Vertical selection in the presence of unlabeled verticals

paper_content:

Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive cross-vertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals
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paper_title: Learning query intent from regularized click graphs

paper_content:

This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words/phrases as features can work remarkably well.
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paper_title: Integration of news content into web results

paper_content:

Aggregated search refers to the integration of content from specialized corpora or verticals into web search results. Aggregation improves search when the user has vertical intent but may not be aware of or desire vertical search. In this paper, we address the issue of integrating search results from a news vertical into web search results. News is particularly challenging because, given a query, the appropriate decision---to integrate news content or not---changes with time. Our system adapts to news intent in two ways. First, by inspecting the dynamics of the news collection and query volume, we can track development of and interest in topics. Second, by using click feedback, we can quickly recover from system errors. We define several click-based metrics which allow a system to be monitored and tuned without annotator effort.
---
paper_title: Sources of evidence for vertical selection

paper_content:

Web search providers often include search services for domain-specific subcollections, called verticals, such as news, images, videos, job postings, company summaries, and artist profiles. We address the problem of vertical selection, predicting relevant verticals (if any) for queries issued to the search engine's main web search page. In contrast to prior query classification and resource selection tasks, vertical selection is associated with unique resources that can inform the classification decision. We focus on three sources of evidence: (1) the query string, from which features are derived independent of external resources, (2) logs of queries previously issued directly to the vertical, and (3) corpora representative of vertical content. We focus on 18 different verticals, which differ in terms of semantics, media type, size, and level of query traffic. We compare our method to prior work in federated search and retrieval effectiveness prediction. An in-depth error analysis reveals unique challenges across different verticals and provides insight into vertical selection for future work.
---
paper_title: Vertical selection in the presence of unlabeled verticals

paper_content:

Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive cross-vertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals
---
paper_title: Factors affecting click-through behavior in aggregated search interfaces

paper_content:

An aggregated search interface is designed to integrate search results from different sources (web, image, video, blog, etc) into a single result page. This paper presents two user studies investigating factors affecting users click-through behavior on aggregated search interfaces. We tested two aggregated search interfaces: one where results from the different sources are blended into a single list (called blended), and another, where results from each source are presented in a separate panel (called non-blended). A total of 1,296 search sessions performed by 48 participants were analysed in our study. Our results suggest that 1) the position of search results is significant only in the blended and not in the non-blended design; 2) participants' click-through behavior on videos is different from other sources; and finally 3) capturing a task's orientation towards particular sources is an important factor for further investigation and research.
---
paper_title: An Analysis of Assessor Behavior in Crowdsourced Preference Judgments

paper_content:

We describe a pilot study using Amazon’s Mechanical Turk to collect preference judgments between pairs of full-page layouts including both search results and image results. Specifically, we analyze the behavior of assessors that participated in our study to identify some patterns that may be broadly indicative of unreliable assessments. We believe this analysis can inform future experimental design and analysis when using crowdsourced human judgments.
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paper_title: Learning query intent from regularized click graphs

paper_content:

This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words/phrases as features can work remarkably well.
---
paper_title: Learning to aggregate vertical results into web search results

paper_content:

Aggregated search is the task of integrating results from potentially multiple specialized search services, or verticals, into the Web search results. The task requires predicting not only which verticals to present (the focus of most prior research), but also predicting where in the Web results to present them (i.e., above or below the Web results, or somewhere in between). Learning models to aggregate results from multiple verticals is associated with two major challenges. First, because verticals retrieve different types of results and address different search tasks, results from different verticals are associated with different types of predictive evidence (or features). Second, even when a feature is common across verticals, its predictiveness may be vertical-specific. Therefore, approaches to aggregating vertical results require handling an inconsistent feature representation across verticals, and, potentially, a vertical-specific relationship between features and relevance. We present 3 general approaches that address these challenges in different ways and compare their results across a set of 13 verticals and 1070 queries. We show that the best approaches are those that allow the learning algorithm to learn a vertical-specific relationship between features and relevance.
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paper_title: A probabilistic model based approach for blended search

paper_content:

In this paper, we propose to model the blended search problem by assuming conditional dependencies among queries, VSEs and search results. The probability distributions of this model are learned from search engine query log through unigram language model. Our experimental exploration shows that, (1) a large number of queries in generic Web search have vertical search intentions; and (2) our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the Mean Average Precision (MAP) by as much as 16% compared to traditional Web search without blending.
---
paper_title: Integration of news content into web results

paper_content:

Aggregated search refers to the integration of content from specialized corpora or verticals into web search results. Aggregation improves search when the user has vertical intent but may not be aware of or desire vertical search. In this paper, we address the issue of integrating search results from a news vertical into web search results. News is particularly challenging because, given a query, the appropriate decision---to integrate news content or not---changes with time. Our system adapts to news intent in two ways. First, by inspecting the dynamics of the news collection and query volume, we can track development of and interest in topics. Second, by using click feedback, we can quickly recover from system errors. We define several click-based metrics which allow a system to be monitored and tuned without annotator effort.
---
paper_title: Sources of evidence for vertical selection

paper_content:

Web search providers often include search services for domain-specific subcollections, called verticals, such as news, images, videos, job postings, company summaries, and artist profiles. We address the problem of vertical selection, predicting relevant verticals (if any) for queries issued to the search engine's main web search page. In contrast to prior query classification and resource selection tasks, vertical selection is associated with unique resources that can inform the classification decision. We focus on three sources of evidence: (1) the query string, from which features are derived independent of external resources, (2) logs of queries previously issued directly to the vertical, and (3) corpora representative of vertical content. We focus on 18 different verticals, which differ in terms of semantics, media type, size, and level of query traffic. We compare our method to prior work in federated search and retrieval effectiveness prediction. An in-depth error analysis reveals unique challenges across different verticals and provides insight into vertical selection for future work.
---
paper_title: On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals

paper_content:

Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals could be relevant, which makes the placement of these vertical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities? In this paper we present a machine-learning framework for SERP composition in the presence of multiple relevant verticals. First, instead of using the traditional label generation method of human judgment guidelines and trained judges, we use a randomized online auditioning system that allows us to evaluate triples of the form query, web block, vertical>. We use a pairwise click preference to evaluate whether the web block or the vertical block had a better users' engagement. Next, we use a hinged feature vector that contains features from the web block to create a common reference frame and augment it with features representing the specific vertical judged by the user. A gradient boosted decision tree is then learned from the training data. For the final composition of the SERP, we place a vertical result at a slot if the score is higher than a computed threshold. The thresholds are algorithmically determined to guarantee specific coverage for verticals at each slot. We use correlation of clicks as our offline metric and show that click-preference target has a better correlation than human judgments based models. Furthermore, on online tests for News and Image verticals we show higher user engagement for both head and tail queries.
---
paper_title: Identifying vertical search intention of query through social tagging propagation

paper_content:

A pressing task during the unification process is to identify a user's vertical search intention based on the user's query. In this paper, we propose a novel method to propagate social annotation, which includes user-supplied tag data, to both queries and VSEs for semantically bridging them. Our proposed algorithm consists of three key steps: query annotation, vertical annotation and query intention identification. Our algorithm, referred to as TagQV, verifies that the social tagging can be propagated to represent Web objects such as queries and VSEs besides Web pages. Experiments on real Web search queries demonstrate the effectiveness of TagQV in query intention identification.
---
paper_title: Vertical selection in the presence of unlabeled verticals

paper_content:

Vertical aggregation is the task of incorporating results from specialized search engines or verticals (e.g., images, video, news) into Web search results. Vertical selection is the subtask of deciding, given a query, which verticals, if any, are relevant. State of the art approaches use machine learned models to predict which verticals are relevant to a query. When trained using a large set of labeled data, a machine learned vertical selection model outperforms baselines which require no training data. Unfortunately, whenever a new vertical is introduced, a costly new set of editorial data must be gathered. In this paper, we propose methods for reusing training data from a set of existing (source) verticals to learn a predictive model for a new (target) vertical. We study methods for learning robust, portable, and adaptive cross-vertical models. Experiments show the need to focus on different types of features when maximizing portability (the ability for a single model to make accurate predictions across multiple verticals) than when maximizing adaptability (the ability for a single model to make accurate predictions for a specific vertical). We demonstrate the efficacy of our methods through extensive experimentation for 11 verticals
---
paper_title: A Methodology for Evaluating Aggregated Search Results

paper_content:

Aggregated search is the task of incorporating results from different specialized search services, or verticals, into Web search results. While most prior work focuses on deciding which verticals to present, the task of deciding where in the Web results to embed the vertical results has received less attention. We propose a methodology for evaluating an aggregated set of results. Our method elicits a relatively small number of human judgements for a given query and then uses these to facilitate a metric-based evaluation of any possible presentation for the query. An extensive user study with 13 verticals confirms that, when users prefer one presentation of results over another, our metric agrees with the stated preference. By using Amazon's Mechanical Turk, we show that reliable assessments can be obtained quickly and inexpensively.
---
paper_title: Learning to rank for information retrieval

paper_content:

This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.
---
paper_title: Learning query intent from regularized click graphs

paper_content:

This work presents the use of click graphs in improving query intent classifiers, which are critical if vertical search and general-purpose search services are to be offered in a unified user interface. Previous works on query classification have primarily focused on improving feature representation of queries, e.g., by augmenting queries with search engine results. In this work, we investigate a completely orthogonal approach --- instead of enriching feature representation, we aim at drastically increasing the amounts of training data by semi-supervised learning with click graphs. Specifically, we infer class memberships of unlabeled queries from those of labeled ones according to their proximities in a click graph. Moreover, we regularize the learning with click graphs by content-based classification to avoid propagating erroneous labels. We demonstrate the effectiveness of our algorithms in two different applications, product intent and job intent classification. In both cases, we expand the training data with automatically labeled queries by over two orders of magnitude, leading to significant improvements in classification performance. An additional finding is that with a large amount of training data obtained in this fashion, classifiers using only query words/phrases as features can work remarkably well.
---
paper_title: Factors affecting click-through behavior in aggregated search interfaces

paper_content:

An aggregated search interface is designed to integrate search results from different sources (web, image, video, blog, etc) into a single result page. This paper presents two user studies investigating factors affecting users click-through behavior on aggregated search interfaces. We tested two aggregated search interfaces: one where results from the different sources are blended into a single list (called blended), and another, where results from each source are presented in a separate panel (called non-blended). A total of 1,296 search sessions performed by 48 participants were analysed in our study. Our results suggest that 1) the position of search results is significant only in the blended and not in the non-blended design; 2) participants' click-through behavior on videos is different from other sources; and finally 3) capturing a task's orientation towards particular sources is an important factor for further investigation and research.
---
paper_title: Advanced Topics in Information Retrieval

paper_content:

Information retrieval is the science concerned with the effective and efficient retrieval of documents starting from their semantic content. It is employed to fulfill some information need from a large number of digital documents. Given the ever-growing amount of documents available and the heterogeneous data structures used for storage, information retrieval has recently faced and tackled novel applications. In this book, Melucci and Baeza-Yates present a wide-spectrum illustration of recent research results in advanced areas related to information retrieval. Readers will find chapters on e.g. aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation. All chapters are written by well-known researchers, are completely self-contained and comprehensive, and are complemented by an integrated bibliography and subject index. With this selection, the editors provide the most up-to-date survey of topics usually not addressed in depth in traditional (text)books on information retrieval. The presentation is intended for a wide audience of people interested in information retrieval: undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers.
---
paper_title: Advanced Topics in Information Retrieval

paper_content:

Information retrieval is the science concerned with the effective and efficient retrieval of documents starting from their semantic content. It is employed to fulfill some information need from a large number of digital documents. Given the ever-growing amount of documents available and the heterogeneous data structures used for storage, information retrieval has recently faced and tackled novel applications. In this book, Melucci and Baeza-Yates present a wide-spectrum illustration of recent research results in advanced areas related to information retrieval. Readers will find chapters on e.g. aggregated search, digital advertising, digital libraries, discovery of spam and opinions, information retrieval in context, multimedia resource discovery, quantum mechanics applied to information retrieval, scalability challenges in web search engines, and interactive information retrieval evaluation. All chapters are written by well-known researchers, are completely self-contained and comprehensive, and are complemented by an integrated bibliography and subject index. With this selection, the editors provide the most up-to-date survey of topics usually not addressed in depth in traditional (text)books on information retrieval. The presentation is intended for a wide audience of people interested in information retrieval: undergraduate and graduate students, post-doctoral researchers, lecturers, and industrial researchers.
---
paper_title: A Task-Based Evaluation of an Aggregated Search Interface

paper_content:

This paper presents a user study that evaluated the effectiveness of an aggregated search interface in the context of non-navigational search tasks. An experimental system was developed to present search results aggregated from multiple information sources, and compared to a conventional tabbed interface. Sixteen participants were recruited to evaluate the performance of the two interfaces. Our results suggest that the aggregated search interface is a promising way of supporting non-navigational search tasks. The quantity and diversity of the retrieved items which participants accessed to complete a task, increased in the aggregated interface. Participants also found the aggregated presentation easier to access to retrieved items and to find relevant information, compared to the conventional interface.
---
paper_title: Integration of news content into web results

paper_content:

Aggregated search refers to the integration of content from specialized corpora or verticals into web search results. Aggregation improves search when the user has vertical intent but may not be aware of or desire vertical search. In this paper, we address the issue of integrating search results from a news vertical into web search results. News is particularly challenging because, given a query, the appropriate decision---to integrate news content or not---changes with time. Our system adapts to news intent in two ways. First, by inspecting the dynamics of the news collection and query volume, we can track development of and interest in topics. Second, by using click feedback, we can quickly recover from system errors. We define several click-based metrics which allow a system to be monitored and tuned without annotator effort.
---
paper_title: Accurately Interpreting Clickthrough Data as Implicit Feedback

paper_content:

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.
---
paper_title: Are people biased in their use of search engines?

paper_content:

Assessing user search behavior when deciding which links to follow in rank-ordered results lists.
---
paper_title: Identifying "best bet" web search results by mining past user behavior

paper_content:

The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special handling of the top web search result for some queries. We propose an effective approach of leveraging millions of past user interactions with a web search engine to automatically detect "best bet" top results preferred by majority of users. Interestingly, this problem can be more effectively addressed with classification than using state-of-the-art general ranking methods. Furthermore, we show that our general machine learning approach achieves precision comparable to a heavily tuned, domain-specific algorithm, with significantly higher coverage. Our experiments over millions of user interactions for thousands of queries demonstrate the effectiveness and robustness of our techniques.
---
paper_title: An eye tracking study of the effect of target rank on web search

paper_content:

Web search engines present search results in a rank ordered list. This works when what a user wants is near the top, but sometimes the information that the user really wants is located at the bottom of the page. This study examined how users' search behaviors vary when target results were displayed at various positions for informational and navigational tasks. We found that when targets were placed relatively low in the first page of search results, people spent more time searching and were less successful in finding the target, especially for informational tasks. Further analysis of eye movements showed that the decrease in search performance was partially due to the fact that users rarely looked at lower ranking results. The large decrease in performance for informational search is probably because users have high confidence in the search engine's ranking; in contrast to navigational tasks, where the target is more obvious from information presented in the results, in informational tasks, users try out the top ranked results even if these results are perceived as less relevant for the task.
---
paper_title: Factors affecting click-through behavior in aggregated search interfaces

paper_content:

An aggregated search interface is designed to integrate search results from different sources (web, image, video, blog, etc) into a single result page. This paper presents two user studies investigating factors affecting users click-through behavior on aggregated search interfaces. We tested two aggregated search interfaces: one where results from the different sources are blended into a single list (called blended), and another, where results from each source are presented in a separate panel (called non-blended). A total of 1,296 search sessions performed by 48 participants were analysed in our study. Our results suggest that 1) the position of search results is significant only in the blended and not in the non-blended design; 2) participants' click-through behavior on videos is different from other sources; and finally 3) capturing a task's orientation towards particular sources is an important factor for further investigation and research.
---

Title: An Overview of Aggregating Vertical Results into Web Search Results

Section 1: INTRODUCTION
Description 1: Introduce the concept of aggregated search, explain traditional web search versus modern web search integrating verticals, and outline the challenges faced.

Section 2: BACKGROUND
Description 2: Provide an analog of federated search, discuss the history and examples of federated and metasearch systems, and introduce related concepts.

Section 3: Constructing Representative Vertical
Description 3: Discuss the need for proper vertical representation in aggregated search, methods for constructing vertical representations, and techniques for sampling documents.

Section 4: Resource Selection in Federated Search
Description 4: Present approaches analogous to vertical selection in aggregated search, discuss document and collection scoring techniques, and introduce algorithms like ReDDE and gGIOSS.

Section 5: Query-classification into Topical Categories
Description 5: Explain the importance of query classification, review prior work in query classification, and describe methods to use query-logs and corpus-based evidence.

Section 6: Vertical Selection Approaches
Description 6: Describe methods for selecting relevant verticals, discuss evidence sources like query string features, query-log features, and corpora representation, and review adaptive algorithms for vertical selection.

Section 7: Evaluation
Description 7: Outline various methods to evaluate aggregated search, focusing on user feedback, precision and recall measures, and comparison of vertical selection approaches.

Section 8: AGGREGATED SEARCH INTERFACES
Description 8: Differentiate between blended and non-blended search interface designs, discuss methodologies and findings from relevant studies, and explore user behavior towards result presentation.

Section 9: Blended Design
Description 9: Delve deeper into blended design, its implementation across search engines, machine learning approaches for blending, and improvements in search relevance and user satisfaction.

Section 10: Non-Blended Design
Description 10: Describe the non-blended design layout, explain how vertical results are displayed separately, and compare its effectiveness to blended design.

Section 11: Viewing Results
Description 11: Summarize findings from eye-tracking studies on result presentation, and compare user scanning patterns in conventional and aggregated search interfaces.

Section 12: User Behavior for Result Presentation
Description 12: Examine the impact of vertical types, position, and task orientation on user behavior and click-through rates, and discuss relevant studies and their findings.