short-paper

Rethinking Correlation-based Item-Item Similarities for Recommender Systems

Author:

Katsuhiko HayashiAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2287 - 2291

https://doi.org/10.1145/3477495.3532055

Published: 07 July 2022 Publication History

Abstract

This paper studies correlation-based item-item similarity measures for recommendation systems. While current research on recommender systems is directed toward deep learning-based approaches, nearest neighbor methods have been still used extensively in commercial recommender systems due to their simplicity. A crucial step in item-based nearest neighbor methods is to compute similarities between items, which are generally estimated through correlation measures like Pearson. The purpose of this paper is to re-investigate the effectiveness of correlation-based nearest neighbor methods on several benchmark datasets that have been used for recommendation evaluation in recent years. This paper also provides a more effective estimation method for correlation measures than the classical Pearson correlation coefficient and shows that this leads to significant improvements in recommendation performance.

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Cited By

Abdalla HAmer AAmer YNguyen LAl-Maqaleh B(2023)Boosting the Item-Based Collaborative Filtering Model with Novel Similarity MeasuresInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00299-216:1Online publication date: 29-Jul-2023
https://doi.org/10.1007/s44196-023-00299-2

Index Terms

Rethinking Correlation-based Item-Item Similarities for Recommender Systems
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Learning from implicit feedback
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Similarity measures
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 ACM.

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2022

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SIGIR '22

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Abdalla HAmer AAmer YNguyen LAl-Maqaleh B(2023)Boosting the Item-Based Collaborative Filtering Model with Novel Similarity MeasuresInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00299-216:1Online publication date: 29-Jul-2023
https://doi.org/10.1007/s44196-023-00299-2

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