research-article

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

Authors:

Alan HanjalicAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 47, Issue 1

Article No.: 3, Pages 1 - 45

https://doi.org/10.1145/2556270

Published: 01 May 2014 Publication History

Abstract

Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

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Index Terms

Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 47, Issue 1

July 2014

551 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2620784

Issue’s Table of Contents

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 2014

Accepted: 01 November 2013

Revised: 01 September 2013

Received: 01 March 2013

Published in CSUR Volume 47, Issue 1

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