research-article

SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System

Authors:

Hui XiongAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 2

Article No.: 56, Pages 1 - 32

https://doi.org/10.1145/3626194

Published: 07 November 2023 Publication History

Abstract

The recent development of recommender systems has a focus on collaborative ranking, which provides users with a sorted list rather than rating prediction. The sorted item lists can more directly reflect the preferences for users and usually perform better than rating prediction in practice. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” and unobserved data due to the precondition of the entire list permutation. To this end, in this article, we propose a novel setwise Bayesian approach for collaborative ranking, namely, SetRank, to inherently accommodate the characteristics of user feedback in recommender systems. SetRank aims to maximize the posterior probability of novel setwise preference structures and three implementations for SetRank are presented. We also theoretically prove that the bound of excess risk in SetRank can be proportional to \(\sqrt {M/N}\), where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

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Cited By

Zhu YWang CZhang QXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Graph Signal Diffusion Model for Collaborative FilteringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657759(1380-1390)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657759
Wu WWang CShen DQin CChen LXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657724(1242-1252)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657724
Hu SWang CQin CZhu HXiong H(2024)Super-Node Generation for GNN-Based Recommender Systems: Enhancing Distant Node Integration via Graph CoarseningDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_24(353-363)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-981-97-5572-1_24

Index Terms

SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 2

March 2024

897 pages

EISSN:1558-2868

DOI:10.1145/3618075

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 07 November 2023

Online AM: 03 October 2023

Accepted: 17 September 2023

Revised: 25 April 2023

Received: 12 July 2021

Published in TOIS Volume 42, Issue 2

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China Postdoctoral Science Foundation
Science and Technology Planning Project of Guangdong Province
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National Natural Science Foundation of China

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Cited By

Zhu YWang CZhang QXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Graph Signal Diffusion Model for Collaborative FilteringProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657759(1380-1390)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657759
Wu WWang CShen DQin CChen LXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657724(1242-1252)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657724
Hu SWang CQin CZhu HXiong H(2024)Super-Node Generation for GNN-Based Recommender Systems: Enhancing Distant Node Integration via Graph CoarseningDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_24(353-363)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-981-97-5572-1_24

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