Mobile Recommender Systems Methods: An Overview
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paper_title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

paper_content:

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
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paper_title: Collaborative and Content-based Filtering for Item Recommendation on Social Bookmarking Websites

paper_content:

Social bookmarking websites allow users to store, organize, and search bookmarks of web pages. Users of these services can annotate their bookmarks by using informal tags and other metadata, such as titles, descriptions, etc. In this paper, we focus on the task of item recommendation for social bookmarking websites, i.e. predicting which unseen bookmarks a user might like based on his or her profile. We examine how we can incorporate the tags and other metadata into a nearest-neighbor collaborative filtering (CF) algorithm, by replacing the traditional usage-based similarity metrics by tag overlap, and by fusing tag-based similarity with usage-based similarity. In addition, we perform experiments with content-based filtering by using the metadata content to recommend interesting items. We generate recommendations directly based on KullbackLeibler divergence of the metadata language models, and we explore the use of this metadata in calculating user and item similarities. We perform our experiments on three data sets from two di erent domains: Delicious, CiteULike and BibSonomy.
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paper_title: Empirical Analysis of Predictive Algorithms for Collaborative Filtering

paper_content:

Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. ::: ::: Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metr rics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector-similarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time.
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paper_title: Text-learning and related intelligent agents: a survey

paper_content:

In surveying current research in the development of text-learning intelligent agents, the author focuses on three key criteria: what representation the particular application uses for documents, how it selects features, and what learning algorithm it uses. She then describes Personal WebWatcher, a content-based intelligent agent that uses text-learning for user-customized Web browsing.
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paper_title: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

paper_content:

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
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paper_title: Content-based recommendation systems

paper_content:

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.
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paper_title: A Framework for Collaborative, Content-Based and Demographic Filtering

paper_content:

We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.
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paper_title: A Framework for Collaborative, Content-Based and Demographic Filtering

paper_content:

We discuss learning a profile of user interests for recommending information sources such as Web pages or news articles. We describe the types of information available to determine whether to recommend a particular page to a particular user. This information includes the content of the page, the ratings of the user on other pages and the contents of these pages, the ratings given to that page by other users and the ratings of these other users on other pages and demographic information about users. We describe how each type of information may be used individually and then discuss an approach to combining recommendations from multiple sources. We illustrate each approach and the combined approach in the context of recommending restaurants.
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paper_title: Modern Information Retrieval

paper_content:

From the Publisher: ::: This is a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective. The advent of the Internet and the enormous increase in volume of electronically stored information generally has led to substantial work on IR from the computer science perspective - this book provides an up-to-date student oriented treatment of the subject.
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paper_title: A contextual-bandit approach to personalized news article recommendation

paper_content:

Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.
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paper_title: Personalized recommendation on dynamic content using predictive bilinear models

paper_content:

In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. We propose a feature-based machine learning approach to personalized recommendation that is capable of handling the cold-start issue effectively. We maintain profiles of content of interest, in which temporal characteristics of the content, e.g. popularity and freshness, are updated in real-time manner. We also maintain profiles of users including demographic information and a summary of user activities within Yahoo! properties. Based on all features in user and content profiles, we develop predictive bilinear regression models to provide accurate personalized recommendations of new items for both existing and new users. This approach results in an offline model with light computational overhead compared with other recommender systems that require online re-training. The proposed framework is general and flexible for other personalized tasks. The superior performance of our approach is verified on a large-scale data set collected from the Today-Module on Yahoo! Front Page, with comparison against six competitive approaches.
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Title: Mobile Recommender Systems Methods: An Overview

Section 1: Introduction
Description 1: Describe the general concept of Recommender Systems (RS), their classification, and integration into Personalized Access Systems.

Section 2: Collaborative Filtering
Description 2: Explain Collaborative Filtering (CF) including its methods, advantages, and limitations.

Section 3: Content-Based Filtering
Description 3: Detail the Content-Based Filtering (CBF) approach, techniques used, and its strengths and weaknesses.

Section 4: Hybrid Approach
Description 4: Discuss the Hybrid Approach combining CF and CBF, different methods of integration, and their benefits.

Section 5: Extending the CBF with properties of CF
Description 5: Explore methods that use CBF extended with CF properties and their impact on the cold start problem.

Section 6: Extending the CF with the properties of CBF
Description 6: Discuss techniques where CF is enhanced with CBF properties, including examples such as clustering based on content.

Section 7: Unifying CBF approach and CF
Description 7: Describe approaches that unify CBF and CF into a single model, providing examples and discussing the efficiency.

Section 8: Discussion
Description 8: Present a synthesis of various recommendation approaches, summarizing their advantages and disadvantages, especially in dynamic content environments.