article

Item-based top-N recommendation algorithms

Authors:

Mukund Deshpande,

George KarypisAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 22, Issue 1

Pages 143 - 177

https://doi.org/10.1145/963770.963776

Published: 01 January 2004 Publication History

Abstract

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.

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Index Terms

Item-based top-N recommendation algorithms
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 22, Issue 1

January 2004

177 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/963770

Issue’s Table of Contents

Copyright © 2004 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 January 2004

Published in TOIS Volume 22, Issue 1

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Ye FLu XLi HChen Z(2024)Transfer learning from rating prediction to Top-k recommendationPLOS ONE10.1371/journal.pone.030024019:3(e0300240)Online publication date: 28-Mar-2024
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