Computing Recommendations with Collaborative Filtering

Computing Recommendations with Collaborative Filtering

Neal Lathia (University College London, UK)
DOI: 10.4018/978-1-60566-306-7.ch002
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Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people: those who have had similar opinions in the past will continue sharing the same tastes in the future. Collaborative filtering, the dominant algorithm underlying recommender systems, uses a model of its users, contained within profiles, in order to guide what interactions should be allowed, and how these interactions translate first into predicted ratings, and then into recommendations. In this chapter, the authors introduce the various approaches that have been adopted when designing collaborative filtering algorithms, and how they differ from one another in the way they make use of the available user information. They then explore how these systems are evaluated, and highlight a number of problems that prevent recommendations from being suitably computed, before looking at the how current trends in recommender system research are projecting towards future developments.
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Recommender systems are experiencing a growing presence on the Internet; they have evolved from being interesting additions of e-commerce web sites into essential components and, in some cases, the core of online businesses. The success of these systems stems from the underlying algorithm, based on collaborative filtering, which re-enacts the way humans exchange recommendations in a way that can be scaled to communities of millions of online users. Users of these systems will thus see personalized, unique, and interest-based recommendations presented to them computed according to the opinions of the other users in the system, and can actively contribute to other’s recommendations by inputting their own ratings.

This chapter introduces recommender systems and the algorithms, based on collaborative filtering, that fuel the success these systems are experiencing in current online applications. There are a number of methods that have been applied when designing filtering algorithms, but they all share a common assumption: the users, and the interactions between them, can be modeled in such a way that it is possible to filter content based on the responses they input.

In particular, the objectives of this chapter can be decomposed into a number of questions:

  • Why do we need recommender systems; what problem do they address?

  • How are recommendations generated? This question explores collaborative filtering: what it is, how it works, and how different fields of research have led collaborative filtering to be categorized into memory- and model-based approaches.

  • How are recommender systems evaluated? In particular, what problems do these systems face, and how does research address these problems? Lastly,

  • What are the current future directions of recommender system research?

We explore these questions by considering the participants of a recommender system as members of a community of users. This method highlights the importance of user models within recommender systems, both as a means of reasoning about the underlying operations on the data and building a system that end-users will respond positively to. However, we begin by looking at the motivating problems and history of these systems.



As the Internet grows, forever broadening both the range and diversity of information that it makes accessible to its users, a new problem arises: the amount of information available, and the rate at which new information is produced, becomes too great for individuals to sift through it all and find relevant resources. Resources may include, but are not limited to, movies, music, products of e-commerce catalogues, blogs, news articles and documents. Users, unable to dedicate the time to browse all that is available, are thus confronted with the problem of information overload, and the sheer abundance of information diminishes users’ ability to identify what would be most useful and valuable to each of their needs.

Recommender systems, based on the principles of collaborative filtering, have been developed in response to information overload, by acting as a decision-aiding tool. However, recommender systems break away from merely helping users search for content towards providing interest-based, personalized content without requiring any search query. Recommender systems diverge from traditional information retrieval by building long term models of each user’s preferences, and selectively combining different users’ opinions in order to provide each user with unique recommendations.

Complete Chapter List

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Table of Contents
Bamshad Mobasher
Max Chevalier, Christine Julien, Chantal Soule-Dupuy
Chapter 1
Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative... Sample PDF
State-of-the-Art Recommender Systems
Chapter 2
Neal Lathia
Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people... Sample PDF
Computing Recommendations with Collaborative Filtering
Chapter 3
Edwin Simpson, Mark H. Butler
The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of... Sample PDF
Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling
Chapter 4
Adaptive User Profiles  (pages 65-87)
Steve Cayzer, Elke Michlmayr
A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant... Sample PDF
Adaptive User Profiles
Chapter 5
Eugene Santos Jr., Hien Nguyen
In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a... Sample PDF
Modeling Users for Adaptive Information Retrieval by Capturing User Intent
Chapter 6
Mihaela Brut, Florence Sedes, Corinne Zayani
Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents... Sample PDF
Ontology-Based User Competencies Modeling for E-Learning Recommender Systems
Chapter 7
Colum Foley, Alan F. Smeaton, Gareth J.F. Jones
Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an... Sample PDF
Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment
Chapter 8
Charles Delalonde, Eddie Soulier
This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The... Sample PDF
DemonD: A Social Search Engine Built Upon the Actor-Network Theory
Chapter 9
Hager Karoui
In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference... Sample PDF
COBRAS: Cooperative CBR Bibliographic Recommender System
Chapter 10
Zehra Cataltepe, Berna Altinel
As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative... Sample PDF
Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity
Chapter 11
Nima Taghipour, Ahmad Kardan
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender... Sample PDF
Web Content Recommendation Methods Based on Reinforcement Learning
Chapter 12
Angela Carrillo-Ramos, Manuele Kirsch Pinheiro, Marlène Villanova-Oliver, Jérôme Gensel, Yolande Berbers
The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is... Sample PDF
Collaborating Agents for Adaptation to Mobile Users
Chapter 13
Cristina Gena, Liliana Ardissono
This chapter describes the user-centered design approach we adopted in the development and evaluation of an adaptive Web site. The development of... Sample PDF
A User-Centered Approach to the Retrieval of Information in an Adaptive Web Site
Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently... Sample PDF
Personalized Information Retrieval in a Semantic-Based Learning Environment
Chapter 15
Hanh Huu Hoang, Tho Manh Nguyen, A Min Tjoa
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding... Sample PDF
A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval
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