Critiquing-based recommenders: survey and emerging trends
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paper_title: A Comparative Study of Compound Critique Generation in Conversational Recommender Systems

paper_content:

Critiquing techniques provide an easy way for users to feedback their preferences over one or several attributes of the products in a conversational recommender system. While unit critiques only allow users to critique one attribute of the products each time, a well-generated set of compound critiques enables users to input their preferences on several attributes at the same time, and can potentially shorten the interaction cycles in finding the target products. As a result, the dynamic generation of compound critiques is a critical issue for designing the critique-based conversational recommender systems. In earlier research the Apriori algorithm has been adopted to generate compound critiques from the given data set. In this paper we propose an alternative approach for generating compound critiques based on the multi-attribute utility theory (MAUT). Our approach automatically updates the weights of the product attributes as the result of the interactive critiquing process. This modification of weights is then used to determine the compound critiques according to those products with the highest utility values. Our experiments show that the compound critiques generated by this approach are more efficient in helping users find their target products than those generated by the Apriori algorithm. © Springer-Verlag Berlin Heidelberg 2006.
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paper_title: Recommender systems: from algorithms to user experience

paper_content:

Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
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paper_title: A Personalized System for Conversational Recommendations

paper_content:

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.
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paper_title: Evaluating Preference-based Search Tools : a Tale of Two Approaches

paper_content:

People frequently use the world-wide web to find their most preferred item among a large range of options. We call this task preference-based search. The most common tool for preference-based search on the WWW today obtains users' preferences by asking them to fill in a form. It then returns a list of items that most closely match these preferences. Recently, several researchers have proposed tools for preference-based search that elicit preferences from the critiques a user actively makes on examples shown to them. We carried out a user study in order to compare the performance of traditional preference-based search tools using form-filling with two different versions of an example-critiquing tool. The results show that example critiquing achieves almost three times the decision accuracy, while requiring only slightly higher interaction effort.
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paper_title: Context-dependent preferences

paper_content:

The standard theory of choice---based on value maximization---associates with each option a real value such that, given an offered set, the decision maker chooses the option with the highest value. Despite its simplicity and intuitive appeal, there is a growing body of data that is inconsistent with this theory. In particular, the relative attractiveness of x compared to y often depends on the presence or absence of a third option z, and the "market share" of an option can actually be increased by enlarging the offered set. We review recent empirical findings that are inconsistent with value maximization, and present a context-dependent model that expresses the value of each option as an additive combination of two components: a contingent weighting process that captures the effect of the background context, and a binary comparison process that describes the effect of the local context. The model accounts for observed violations of the standard theory and provides a framework for analyzing context-dependent preferences.
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paper_title: The FindMe Approach to Assisted Browsing

paper_content:

While the explosion of online information has introduced new opportunities for finding and using electronic data, it has also underscored the problem of isolating useful information and making sense of large, multidimensional information spaces. In response to this problem, we have developed an approach to building data tour guides, called FindMe systems. These programs know enough about an information space to help users navigate through it, making sure they not only come away with useful information but also insights into the structure of the information space itself. In these systems, we have combined the idea of instance-based browsing, which involves structuring retrieval around the critiquing of previously retrieved examples, and retrieval strategies, or knowledge-based heuristics for finding relevant information. This article illustrates these techniques with examples of working FindMe systems, and describes the similarities and differences between them. FindMe tour guides help users to select the perfect car, movie, restaurant, stereo or apartment on the World Wide Web.
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paper_title: Interactive Assessment of User Preference Models: The Automated Travel Assistant

paper_content:

This paper presents the candidate/critique model of interactive problem solving, in which an automated problem solver communicates candidate solutions to the user and the user critiques those solutions. The system starts with minimal information about the user’s preferences, and preferences are elicited and inferred incrementally by analyzing the critiques. The system’s goal is to present “good” candidates to the user, but to do so it must learn as much as possible about his preferences in order to improve its choice of candidates in subsequent iterations. This system contrasts with traditional decision-analytic and planning frameworks in which a complete model is elicited beforehand or is constructed by a human expert. The paper presents the Automated Travel Assistant, an implemented prototype of the model that interactively builds flight itineraries using realtime airline information. The ATA is available on the World Wide Web and has had over 4000 users between May and October 1996.
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paper_title: Enriching buyers' experiences: the SmartClient approach

paper_content:

In electronic commerce, a satisfying buyer experience is a key competitive element. We show new techniques for better adapting interaction with an electronic catalog system to actual buying behavior. Our model replaces the sequential separation of needs identification and product brokering with a conversation in which both processes occur simultaneously. This conversation supports the buyer in formulating his or her needs, and in deciding which criteria to apply in selecting a product to buy. We have experimented with this approach in the area of travel planning and developed a system called SmartClient Travel which supports this process. It includes tools for need identification, visualization of alternatives, and choosing the most suitable one. We describe the system and its implementation, and report on user studies showing its advantages for electronic catalogs.
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paper_title: Intelligent profiling by example

paper_content:

The Apt Decision agent learns user preferences in the domain of rental real estate by observing the user's critique of apartment features. Users provide a small number of criteria in the initial interaction, receive a display of sample apartments, and then react to any feature of any apartment independently, in any order. Users learn which features are important to them as they discover the details of specific apartments. The agent uses interactive learning techniques to build a profile of user preferences, which can then be saved and used in further retrievals. Because the user's actions in specifying preferences are also used by the agent to create a profile, the result is an agent that builds a profile without redundant or unnecessary effort on the user's part.
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paper_title: Consumer-centered interfaces: customizing online travel planning

paper_content:

A large number of online reservation systems exist currently on the Web. They all share the same implementation approach: the user has to input his/her travel preferences then choose from existing possibilities. The consumer, reduced to a passive observer, has no contribution to the search process and also no help from the system in deciding the best solution. My work is proposing new tools for improving the facilities offered by the online reservation systems. The customers, identified with active users, are involved in useful interactions with the systems. Three main steps are proposed for achieving this goal: trip configuration, tools for tradeoff and decision support and interface customization.
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paper_title: A Personalized System for Conversational Recommendations

paper_content:

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.
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paper_title: SmartClients: Constraint Satisfaction as a Paradigm for Scaleable Intelligent Information Systems

paper_content:

Many information systems are used in a problem solving context. Examples are travel planning systems, catalogs in electronic commerce, or agenda planning systems. They can be made more useful by integrating problem-solving capabilities into the information systems. This poses the challenge of scaleability: when hundreds of users access a server at the same time, it is important to avoid excessive computational load.We present the concept of SmartClients: lightweight problem-solving agents based on constraint satisfaction which can carry out the computation- and communication-intensive tasks on the user's computer. We present an example of an air travel planning system based on this technology.
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paper_title: Solution generation with qualitative models of preferences

paper_content:

Reference LIA-ARTICLE-2004-002View record in Web of Science Record created on 2006-12-13, modified on 2017-05-12
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paper_title: Java Constraint Library: bringing constraints technology on the Internet using the Java language

paper_content:

Distributed problem solving on the web is becoming more and more important. Client server architectures are often confronted with server overload. The process of browsing a large number of alternative solutions is particularly tedious. In this paper, we present s methodology for distributing the computation between server and client. The ides is to formalise the problem as s constraint satisfaction problem (CSP). This formalisation supports a natural decomposition of the task into two subtasks: generation of the CSP by the server from its database, and generating and browsing the solutions on the client. In this way, the browsing process runs locally and can be very fast, while the server is only accessed once during the pro. ceas. We provide the :lava Constraint library (JCL) for implementing the agent that solves the CSP on the client. We illustrate the concept on the example of planning a~ travel.
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paper_title: Usability Guidelines for Product Recommenders Based on Example Critiquing Research

paper_content:

Over the past decade, our group has developed a suite of decision tools based on example critiquing to help users find their preferred products in e-commerce environments. In this chapter, we survey important usability research work relative to example critiquing and summarize the major results by deriving a set of usability guidelines. Our survey is focused on three key interaction activities between the user and the system: the initial preference elicitation process, the preference revision process, and the presentation of the systems recommendation results. To provide a basis for the derivation of the guidelines, we developed a multi-objective framework of three interacting criteria: accuracy, confidence, and effort (ACE). We use this framework to analyze our past work and provide a specific context for each guideline: when the system should maximize its ability to increase users’ decision accuracy, when to increase user confidence, and when to minimize the interaction effort for the users. Due to the general nature of this multi-criteria model, the set of guidelines that we propose can be used to ease the usability engineering process of other recommender systems, especially those used in e-commerce environments. The ACE framework presented here is also the first in the field to evaluate the performance of preference-based recommenders from a user-centric point of view.
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paper_title: Interaction design guidelines on critiquing-based recommender systems

paper_content:

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user's preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverage--one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid--system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users' decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users' decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender system.
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paper_title: Preference-based Organization Interfaces: Aiding User Critiques in Recommender Systems

paper_content:

Users' critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system's accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users' intended critiques and recommendation accuracy in locating users' target choices. In addition, it can potentially most efficiently save real users' interaction effort in decision making.
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paper_title: Integrating tradeoff support in product search tools for e-commerce sites

paper_content:

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based search tools were likely to be useful particularly for extending the scope of consumer e-commerce to more complex products where decision making is critical. This paper presents results from a follow-up user study quantifying the benefits of tradeoff support. Users were able to refine the quality of their preference structures and improve decision accuracy by up to 57% after performing tradeoff tasks. Tradeoff support also significantly increased users' confidence in their choices. Together, these two studies show that example critiquing enables users to more accurately find what they want and be confident in their choices, while only requiring a level of effort that is comparable to the ranked list interface.
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paper_title: Evaluating example-based search tools

paper_content:

A crucial element in consumer electronic commerce is a catalog tool that not only finds the product for the user, but also convinces him that he has made the best choice. To do that, it is important to show him ample choices while keeping his interaction effort below an acceptable limit. Among the various interaction models used in operational e-commerce sites, ranked lists are by far the most popular tool for product navigation and selection. However, as the number of product features and the complexity of user's criteria increase, a ranked list's efficiency becomes less satisfactory. As an alternative, research groups from the intelligent user interface community have developed various example-based search tools, including SmartClient from our laboratory. These tools not only perform personalized search, but also support tradeoff analysis. However, despite the academic interest, example-based search paradigms have not been widely adopted in practice. We have examined the usability of such tools on a variety of tasks involving selection and tradeoff. The studies clearly show that example-based search is comparable to ranked lists on simple tasks, but significantly reduces the error rate and search time when complex tradeoffs are involved. This shows that such tools are likely to be useful particularly for extending the scope of consumer e-commerce to more complex products.
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paper_title: Experiments in dynamic critiquing

paper_content:

Conversational recommender systems are commonly used to help users to navigate through complex product-spaces by alternatively making product suggestions and soliciting user feedback in order to guide subsequent suggestions. Recently, there has been a surge of interest in developing effective interfaces that support user interaction in domains of limited user expertise. Critiquing has proven to be a popular and successful user feedback mechanism in this regard, but is typically limited to the modification of single features. We review a novel approach to critiquing, dynamic critiquing, that allows users to modify multiple features simultaneously by choosing from a range of so-called compound critiques that are automatically proposed based on their current position within the product-space. In addition, we introduce the results of an important new live-user study that evaluates the practical benefits of dynamic critiquing.
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paper_title: Knowledge-Based Navigation of Complex Information Spaces

paper_content:

While the explosion of on-line information has brought new opportunities for finding and using electronic data, it has also brought to the forefront the problem of isolating useful information and making sense of large multidimension information spaces. We have built several developed an approach to building data "tour guides," called FINDME systems. These programs know enough about an information space to be able to help a user navigate through it. The user not only comes away with items of useful information but also insights into the structure of the information space itself. In these systems, we have combined ideas of instance-based browsing, structuring retrieval around the critiquing of previously-retrieved examples, and retrieval strategies, knowledge-based heuristics for finding relevant information. We illustrate these techniques with several examples, concentrating especially on the RENTME system, a FINDME system for helping users find suitable rental apartments in the Chicago metropolitan area.
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paper_title: The FindMe Approach to Assisted Browsing

paper_content:

While the explosion of online information has introduced new opportunities for finding and using electronic data, it has also underscored the problem of isolating useful information and making sense of large, multidimensional information spaces. In response to this problem, we have developed an approach to building data tour guides, called FindMe systems. These programs know enough about an information space to help users navigate through it, making sure they not only come away with useful information but also insights into the structure of the information space itself. In these systems, we have combined the idea of instance-based browsing, which involves structuring retrieval around the critiquing of previously retrieved examples, and retrieval strategies, or knowledge-based heuristics for finding relevant information. This article illustrates these techniques with examples of working FindMe systems, and describes the similarities and differences between them. FindMe tour guides help users to select the perfect car, movie, restaurant, stereo or apartment on the World Wide Web.
---
paper_title: Interactive Assessment of User Preference Models: The Automated Travel Assistant

paper_content:

This paper presents the candidate/critique model of interactive problem solving, in which an automated problem solver communicates candidate solutions to the user and the user critiques those solutions. The system starts with minimal information about the user’s preferences, and preferences are elicited and inferred incrementally by analyzing the critiques. The system’s goal is to present “good” candidates to the user, but to do so it must learn as much as possible about his preferences in order to improve its choice of candidates in subsequent iterations. This system contrasts with traditional decision-analytic and planning frameworks in which a complete model is elicited beforehand or is constructed by a human expert. The paper presents the Automated Travel Assistant, an implemented prototype of the model that interactively builds flight itineraries using realtime airline information. The ATA is available on the World Wide Web and has had over 4000 users between May and October 1996.
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paper_title: On the Dynamic Generation of Compound Critiques in Conversational Recommender Systems

paper_content:

Conversational recommender systems help to guide users through a product-space towards a particular product that meets their specific requirements. During the course of a “conversation” with the user the recommender system will suggest certain products and use feedback from the user to refine future suggestions. Critiquing has proven to be a powerful and popular form of feedback. Critiques allow the user to express a preference over part of the feature-space; for example, in a vacation/travel recommender a user might indicate that they are looking for a “less expensive” vacation than the one suggested, thereby critiquing the price feature. Usually the set of critiques that the user can chose from is fixed as part of the basic recommender interface. In this paper we will propose a more dynamic critiquing approach where high-quality critiques are automatically generated during each recommendation cycle from the remaining product-cases. We show that these dynamic critiques can lead to more efficient recommendation performance by helping the user to more rapidly focus in on the right region of the product-space.
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paper_title: A Comparative Study of Compound Critique Generation in Conversational Recommender Systems

paper_content:

Critiquing techniques provide an easy way for users to feedback their preferences over one or several attributes of the products in a conversational recommender system. While unit critiques only allow users to critique one attribute of the products each time, a well-generated set of compound critiques enables users to input their preferences on several attributes at the same time, and can potentially shorten the interaction cycles in finding the target products. As a result, the dynamic generation of compound critiques is a critical issue for designing the critique-based conversational recommender systems. In earlier research the Apriori algorithm has been adopted to generate compound critiques from the given data set. In this paper we propose an alternative approach for generating compound critiques based on the multi-attribute utility theory (MAUT). Our approach automatically updates the weights of the product attributes as the result of the interactive critiquing process. This modification of weights is then used to determine the compound critiques according to those products with the highest utility values. Our experiments show that the compound critiques generated by this approach are more efficient in helping users find their target products than those generated by the Apriori algorithm. © Springer-Verlag Berlin Heidelberg 2006.
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paper_title: Thinking positively - explanatory feedback for conversational recommender systems

paper_content:

When it comes to buying expensive goods people expect to be skillfully steered through the options by well-informed sales assistants that are capable of balancing the user’s many and varied requirements. In addition users often need to be educated about the product-space, especially if they are to come to understand what is available and why certain options are being recommended by the sales-assistant. The same issues arise in interactive recommender systems, our online equivalent of a sales assistant and explanation in recommender systems, as a means to educate users and justify recommendations, is now well accepted. In this paper we focus on a novel approach to explanation. Instead of attempting to justify a particular recommendation we focus on how explanations can help users to understand the recommendation opportunities that remain if the current recommendation should not meet their requirements. We describe how this approach to explanation is tightly coupled with the generation of compound critiques, which act as a form of feedback for the users. And we argue that these explanation-rich critiques have the potential to dramatically improve recommender performance and usability.
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paper_title: Mining association rules between sets of items in large databases

paper_content:

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
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paper_title: On the evaluation of dynamic critiquing: a large-scale user study

paper_content:

Critiquing is an important form of feedback in conversational recommender systems. However, in these systems the user is usually limited to critiquing a single product feature at a time. Recently dynamic critiquing has been proposed to address this shortcoming, by automatically generating compound critiques over multiple features that may be presented to the user at recommendation time. To date a number of different versions of dynamic critiquing have been evaluated in isolation, and with reference to artificial users. In this paper we bring together the main flavors of dynamic critiquing and perform a large-scale comparative evaluation as part of an extensive real-user trial. This evaluation reveals some interesting facts about the way real users interact with critique-based recommenders.
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paper_title: A Comparative Study of Compound Critique Generation in Conversational Recommender Systems

paper_content:

Critiquing techniques provide an easy way for users to feedback their preferences over one or several attributes of the products in a conversational recommender system. While unit critiques only allow users to critique one attribute of the products each time, a well-generated set of compound critiques enables users to input their preferences on several attributes at the same time, and can potentially shorten the interaction cycles in finding the target products. As a result, the dynamic generation of compound critiques is a critical issue for designing the critique-based conversational recommender systems. In earlier research the Apriori algorithm has been adopted to generate compound critiques from the given data set. In this paper we propose an alternative approach for generating compound critiques based on the multi-attribute utility theory (MAUT). Our approach automatically updates the weights of the product attributes as the result of the interactive critiquing process. This modification of weights is then used to determine the compound critiques according to those products with the highest utility values. Our experiments show that the compound critiques generated by this approach are more efficient in helping users find their target products than those generated by the Apriori algorithm. © Springer-Verlag Berlin Heidelberg 2006.
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paper_title: A visual interface for critiquing-based recommender systems

paper_content:

Critiquing-based recommender systems provide an efficient way for users to navigate through complex product spaces even if they are not familiar with the domain details in e-commerce environments. While recent researchers have mainly concentrated on methods for generating high quality compound critiques, to date there has been a lack of comprehensive investigation on the interface design issues. Traditionally the interface is textual, which shows compound critiques in plain text and may not be easily understood. In this paper we propose a new visual interface which represents various critiques by a set of meaningful icons. Results from our real-user evaluation show that the visual interface can improve the performance of critique-based recommenders by attracting users to apply the compound critiques more frequently and reducing users' interaction effort substantially when the product domain is complex. Users' subjective feedback also shows that the visual interface is highly promising in enhancing users' shopping experience.
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paper_title: Preference-based Organization Interfaces: Aiding User Critiques in Recommender Systems

paper_content:

Users' critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system's accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users' intended critiques and recommendation accuracy in locating users' target choices. In addition, it can potentially most efficiently save real users' interaction effort in decision making.
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paper_title: Evaluating compound critiquing recommenders: a real-user study

paper_content:

Conversational recommender systems are designed to help users to more efficiently navigate complex product spaces by alternatively making recommendations and inviting users' feedback. Compound critiquing techniques provide an efficient way for users to feed back their preferences (in terms of several simultaneous product attributes) when interfacing with conversational recommender systems. For example, in the laptop domain a user might wish to express a preference for a laptop that is "Cheaper, Lighter, with a Larger Screen". While recently a number of techniques for dynamically generating compound critiques have been proposed, to date there has been a lack of direct comparison of these approaches in a real-user study. In this paper we will compare two alternative approaches to the dynamic generation of compound critiques based on ideas from data mining and multi-attribute utility theory. We will demonstrate how both approaches support users to more efficiently navigate complex product spaces highlighting, in particular, the influence of product complexity and interface strategy on recommendation performance and user satisfaction.
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paper_title: Mining association rules between sets of items in large databases

paper_content:

We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
---
paper_title: Evaluating Preference-based Search Tools : a Tale of Two Approaches

paper_content:

People frequently use the world-wide web to find their most preferred item among a large range of options. We call this task preference-based search. The most common tool for preference-based search on the WWW today obtains users' preferences by asking them to fill in a form. It then returns a list of items that most closely match these preferences. Recently, several researchers have proposed tools for preference-based search that elicit preferences from the critiques a user actively makes on examples shown to them. We carried out a user study in order to compare the performance of traditional preference-based search tools using form-filling with two different versions of an example-critiquing tool. The results show that example critiquing achieves almost three times the decision accuracy, while requiring only slightly higher interaction effort.
---
paper_title: Preference-based Search using Example-Critiquing with Suggestions

paper_content:

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users' current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.
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paper_title: Experiments in dynamic critiquing

paper_content:

Conversational recommender systems are commonly used to help users to navigate through complex product-spaces by alternatively making product suggestions and soliciting user feedback in order to guide subsequent suggestions. Recently, there has been a surge of interest in developing effective interfaces that support user interaction in domains of limited user expertise. Critiquing has proven to be a popular and successful user feedback mechanism in this regard, but is typically limited to the modification of single features. We review a novel approach to critiquing, dynamic critiquing, that allows users to modify multiple features simultaneously by choosing from a range of so-called compound critiques that are automatically proposed based on their current position within the product-space. In addition, we introduce the results of an important new live-user study that evaluates the practical benefits of dynamic critiquing.
---
paper_title: The FindMe Approach to Assisted Browsing

paper_content:

While the explosion of online information has introduced new opportunities for finding and using electronic data, it has also underscored the problem of isolating useful information and making sense of large, multidimensional information spaces. In response to this problem, we have developed an approach to building data tour guides, called FindMe systems. These programs know enough about an information space to help users navigate through it, making sure they not only come away with useful information but also insights into the structure of the information space itself. In these systems, we have combined the idea of instance-based browsing, which involves structuring retrieval around the critiquing of previously retrieved examples, and retrieval strategies, or knowledge-based heuristics for finding relevant information. This article illustrates these techniques with examples of working FindMe systems, and describes the similarities and differences between them. FindMe tour guides help users to select the perfect car, movie, restaurant, stereo or apartment on the World Wide Web.
---
paper_title: Integrating tradeoff support in product search tools for e-commerce sites

paper_content:

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based search tools were likely to be useful particularly for extending the scope of consumer e-commerce to more complex products where decision making is critical. This paper presents results from a follow-up user study quantifying the benefits of tradeoff support. Users were able to refine the quality of their preference structures and improve decision accuracy by up to 57% after performing tradeoff tasks. Tradeoff support also significantly increased users' confidence in their choices. Together, these two studies show that example critiquing enables users to more accurately find what they want and be confident in their choices, while only requiring a level of effort that is comparable to the ranked list interface.
---
paper_title: Hybrid critiquing-based recommender systems

paper_content:

We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such self-motivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.
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paper_title: Hybrid critiquing-based recommender systems

paper_content:

We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such self-motivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.
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paper_title: Preference-based Organization Interfaces: Aiding User Critiques in Recommender Systems

paper_content:

Users' critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system's accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users' intended critiques and recommendation accuracy in locating users' target choices. In addition, it can potentially most efficiently save real users' interaction effort in decision making.
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paper_title: Hybrid critiquing-based recommender systems

paper_content:

We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such self-motivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.
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paper_title: The evaluation of a hybrid critiquing system with preference-based recommendations organization

paper_content:

The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths from both system-proposed critiques and user self-motivated critiquing facility can highly improve users' subjective perceptions such as their decision confidence and trusting intentions. In this paper, we continue to investigate how to further reduce users' objective decision effort (e.g. time consumption) in such system by increasing the critique prediction accuracy of the system-proposed critiques. By means of real user evaluation, we proved that a new hybrid critiquing system design that integrates the preference-based recommendations organization technique for critiques suggestion can effectively help to increase the proposed critiques' application frequency and significantly contribute to saving users' task time and interaction effort.
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paper_title: Preference-based Organization Interfaces: Aiding User Critiques in Recommender Systems

paper_content:

Users' critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system's accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users' intended critiques and recommendation accuracy in locating users' target choices. In addition, it can potentially most efficiently save real users' interaction effort in decision making.
---
paper_title: The evaluation of a hybrid critiquing system with preference-based recommendations organization

paper_content:

The critiquing-based recommender system mainly aims to guide users to make an accurate and confident decision, while requiring them to consume a low level of effort. We have previously found that the hybrid critiquing system of combining the strengths from both system-proposed critiques and user self-motivated critiquing facility can highly improve users' subjective perceptions such as their decision confidence and trusting intentions. In this paper, we continue to investigate how to further reduce users' objective decision effort (e.g. time consumption) in such system by increasing the critique prediction accuracy of the system-proposed critiques. By means of real user evaluation, we proved that a new hybrid critiquing system design that integrates the preference-based recommendations organization technique for critiques suggestion can effectively help to increase the proposed critiques' application frequency and significantly contribute to saving users' task time and interaction effort.
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paper_title: Adaptive tradeoff explanations in conversational recommenders

paper_content:

The completeness and certainty of a user's preferences may vary during her preference construction process in a conversational recommender. In order to more effectively support users to uncover their hidden criteria and/or solve preference conflicts, we propose to generate adaptive tradeoff explanations in organization-based recommender interfaces, to be conditional on the user's contextual needs. An experiment shows the adaptive element's higher potential to improve recommendation efficiency, relative to methods without this feature.
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paper_title: Navigating the tag genome

paper_content:

Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less "dark" movie. This paper introduces Movie Tuner, a novel interface that supports navigation from one item to nearby items along dimensions represented by tags. Movie Tuner is based on a data structure called the tag genome, which is described in separate work. The tag genome encodes each item's relationship to a common set of tags by applying machine learning algorithms to user-contributed content. The present paper discusses our design of Movie Tuner, including algorithms for navigating to new items and for suggesting tags for navigation. We present the results of a 7-week field trial of 2,531 users of Movie Tuner, and of a survey evaluating users' subjective experience.
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paper_title: Conversational recommenders with adaptive suggestions

paper_content:

We consider a conversational recommender system based on example-critiquing where some recommendations are suggestions aimed at stimulating preference expression to acquire an accurate preference model. User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32]. This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples. We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.
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paper_title: Adaptive tradeoff explanations in conversational recommenders

paper_content:

The completeness and certainty of a user's preferences may vary during her preference construction process in a conversational recommender. In order to more effectively support users to uncover their hidden criteria and/or solve preference conflicts, we propose to generate adaptive tradeoff explanations in organization-based recommender interfaces, to be conditional on the user's contextual needs. An experiment shows the adaptive element's higher potential to improve recommendation efficiency, relative to methods without this feature.
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paper_title: Improving recommender systems with adaptive conversational strategies

paper_content:

Conversational recommender systems (CRSs) assist online users in their information-seeking and decision making tasks by supporting an interactive process. Although these processes could be rather diverse, CRSs typically follow a fixed strategy, e.g., based on critiquing or on iterative query reformulation. In a previous paper, we proposed a novel recommendation model that allows conversational systems to autonomously improve a fixed strategy and eventually learn a better one using reinforcement learning techniques. This strategy is optimal for the given model of the interaction and it is adapted to the users' behaviors. In this paper we validate our approach in an online CRS by means of a user study involving several hundreds of testers. We show that the optimal strategy is different from the fixed one, and supports more effective and efficient interaction sessions.
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paper_title: Critiquing recommenders for public taste products

paper_content:

Critiquing-based recommenders do not require users to state all of their preferences upfront or rate a set of previously experienced products. Compared to other types of recommenders, they require relatively little user effort, especially initially, despite potential accuracy problems. On the other hand, they rely on a set of critiques to elicit users feedback in order to improve accuracy. Thus the better the critiques are, the more accurately and efficiently the system becomes in generating its recommendations. This method has been successfully applied to high-involvement products. However, it was never tested on public taste products such as music, films, perfumes, fashion goods or wine. Indeed our initial trial adapting traditional critiquing methods to this new domain led to unsatisfactory results. This has motivated us to develop a novel approach named "editorial picked critiques" (EPC) that accounts for users' needs for popularity information, editorial suggestions, as well as their needs for personalization and diversity. Through an empirical study, we demonstrate that EPC presents a viable recommender approach and is superior on several dimensions to critiques generated by data mining methods.
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paper_title: Recommender systems: from algorithms to user experience

paper_content:

Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
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Title: Critiquing-based recommenders: survey and emerging trends
Section 1: Introduction
Description 1: Write about the evolution of online product catalogs, the rise of high-risk products, and how critiquing-based recommender systems have emerged to address cold start problems and assist users in making informed and accurate decisions through incremental critiquing.

Section 2: Natural language dialog based systems
Description 2: Discuss systems that engage users in conversational dialog for providing preference feedback, with examples such as ExpertClerk and Adaptive Place Advisor.

Section 3: System-suggested critiquing systems
Description 3: Explain the approach of systems that pro-actively generate critique suggestions based on product knowledge, user preferences, or remaining product availability, including examples like FindMe and Dynamic Critiquing.

Section 4: User-initiated critiquing systems
Description 4: Describe how these systems focus on allowing users to create their own critiques freely, facilitating tradeoff navigation and improving decision accuracy, with examples like Example Critiquing and Flat Finder.

Section 5: Discussion: pros and cons
Description 5: Analyze the advantages and disadvantages of the different critiquing approaches, touching on aspects like user control, critique suggestion accuracy, and the challenges of each method.

Section 6: Towards hybrid critiquing systems
Description 6: Elaborate on the benefits of combining system-suggested and user-initiated critiquing approaches, leading to hybrid systems that enhance decision performance and user satisfaction.

Section 7: Adoption of critiquing-based recommenders in industry
Description 7: Highlight real-world applications of critiquing recommenders, discussing their integration into online platforms like Amazon and Movielens, and their impact on user experience and decision-making.

Section 8: Future trends
Description 8: Suggest potential directions for further research and improvement in critiquing-based recommenders, including enhancements in natural language processing, adaptive elements, and applicability in low-involvement product domains.

Section 9: Conclusion
Description 9: Summarize the development and classification of critiquing-based recommenders, emphasizing the experimental findings and practical benefits, and reiterate the emerging trends and future research opportunities in this area.