research-article

CoFi-points: Collaborative Filtering via Pointwise Preference Learning on User/Item-Set

Authors:

Zhong MingAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 4

Article No.: 41, Pages 1 - 24

https://doi.org/10.1145/3389127

Published: 25 May 2020 Publication History

Abstract

With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users’ preferences from historically observed information. As an effective preference learning technology, collaborative filtering has been widely extended to model the one-class or implicit feedback data, which is known as one-class collaborative filtering (OCCF). For a long time, pairwise ranking-oriented learning scheme has been viewed as a superior solution than the pointwise scheme for OCCF due to its higher accuracy in most cases. However, we argue that with appropriate model design, pointwise preference learning can achieve comparable or even better performance than the counterpart, i.e., pairwise preference learning. In particular, we propose a new preference assumption, i.e., pointwise preference on user/item-set. Based on this new assumption, we develop a novel, simple, and flexible solution called collaborative filtering via pointwise preference learning on user/item-set (CoFi-points). Furthermore, we derive two specific algorithms of CoFi-points with respect to the involved user-set and item-set, i.e., CoFi-points(u) and CoFi-points(i), referring to preference assumptions defined on user-set and item-set, respectively. Finally, we conduct extensive empirical studies on four real-world datasets with the state-of-the-art methods, and find that our solution can achieve very promising performance with respect to several ranking-oriented evaluation metrics.

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Cited By

Lin JHe MPan WMing Z(2023)Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-setsInformation Sciences10.1016/j.ins.2022.11.064621(136-155)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.064
Ma WPan WMing Z(2022)SCF: Structured collaborative filtering with heterogeneous implicit feedbackKnowledge-Based Systems10.1016/j.knosys.2022.109999258(109999)Online publication date: Dec-2022
https://doi.org/10.1016/j.knosys.2022.109999
Tang GZhu XGuo JDietze S(2022)Time enhanced graph neural networks for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2022.109204(109204)Online publication date: Jun-2022
https://doi.org/10.1016/j.knosys.2022.109204

Index Terms

CoFi-points: Collaborative Filtering via Pointwise Preference Learning on User/Item-Set
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 4

Survey Paper and Regular Paper

August 2020

358 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3401889

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2020 ACM.

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Association for Computing Machinery

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Publication History

Published: 25 May 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 February 2020

Received: 01 December 2019

Published in TIST Volume 11, Issue 4

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Cited By

Lin JHe MPan WMing Z(2023)Collaborative filtering with sequential implicit feedback via learning users’ preferences over item-setsInformation Sciences10.1016/j.ins.2022.11.064621(136-155)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.064
Ma WPan WMing Z(2022)SCF: Structured collaborative filtering with heterogeneous implicit feedbackKnowledge-Based Systems10.1016/j.knosys.2022.109999258(109999)Online publication date: Dec-2022
https://doi.org/10.1016/j.knosys.2022.109999
Tang GZhu XGuo JDietze S(2022)Time enhanced graph neural networks for session-based recommendationKnowledge-Based Systems10.1016/j.knosys.2022.109204(109204)Online publication date: Jun-2022
https://doi.org/10.1016/j.knosys.2022.109204

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