State-of-the-Art Recommender Systems

State-of-the-Art Recommender Systems

Laurent Candillier (Orange Labs Lannion, France), Kris Jack (Orange Labs Lannion, France), Françoise Fessant (Orange Labs Lannion, France) and Frank Meyer (Orange Labs Lannion, France)
DOI: 10.4018/978-1-60566-306-7.ch001
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The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative filtering approaches can be distinguished from content-based ones. The former is based on a set of user ratings on items, while the latter uses item content descriptions and user thematic profiles. While collaborative filtering systems often result in better predictive performance, content-based filtering offers solutions to the limitations of collaborative filtering, as well as a natural way to interact with users. These complementary approaches thus motivate the design of hybrid systems. In this chapter, the main algorithmic methods used for recommender systems are presented in a state of the art. The evaluation of recommender systems is currently an important issue. The authors focus on two kinds of evaluations. The first one concerns the performance accuracy: several approaches are compared through experiments on two real movies rating datasets MovieLens and Netflix. The second concerns user satisfaction and for this a hybrid system is implemented and tested with real users.
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There has been a growth in interest in Recommender Systems in the last two decades (Adomavicius & Tuzhilin, 2005), since the appearance of the first papers on this subject in the mid-1990s (Resnick et al., 1994). The aim of such systems is to help users to find items that they should appreciate from huge catalogues.

Items can be of any type, such as films, music, books, web pages, online news, jokes, restaurants and even lifestyles. Recommender systems help users to find such items of interest based on some information about their historical preferences. (Nageswara Rao & Talwar, 2008) inventory a varied list of existing recommender systems and their application domain that have been developed in the academia and in the industry.

Three types of recommender systems are commonly implemented:

  • collaborative filtering;

  • content-based filtering;

  • and hybrid filtering.

These systems have, however, their inherent strengths and weaknesses. The recommendation system designer must select which strategy is most appropriate given a particular problem. For example, if little item appreciation data is available then a collaborative filtering approach is unlikely to be well suited to the problem. Likewise, if item descriptions are not available then content-based filtering approaches will have trouble. The choice of approach can also have important effects upon user satisfaction. The designer must take all of these factors into account in the early conception of the system.

This chapter gives an overview of the state-of-the-art in recommender systems, considering both motivations behind them and their underlying strategies. The three previously mentioned recommendation approaches are then described in detail, providing a practical basis for going on to create such systems. The results from a number of experiments, carried out in the field of film recommendation, are then presented and discussed, making two novel contributions to the field. First, a number of baseline tests are carried out in which numerous recommendation strategy approaches are compared, allowing the reader to see their strengths and weaknesses in detail and on a level playing field. Second, a novel hybrid recommendation system is introduced that is tested with real users. The results of the testing demonstrate the importance of user satisfaction in recommendation system design.


Recommender System Approaches

As previously introduced, recommender systems are usually classified into three categories: collaborative, content-based and hybrid filtering, based on how recommendations are made. We review in this section the main algorithmic approaches.

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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