research-article

SPR: : Similarity pairwise ranking for personalized recommendation

Authors:

Ruiyi WangAuthors Info & Claims

Volume 239, Issue C

https://doi.org/10.1016/j.knosys.2021.107828

Published: 05 March 2022 Publication History

Abstract

Bayesian personalized ranking (BPR) has been proposed as an effective method to model pairwise learning, and it is widely used in many personalized recommender systems. However, the effectiveness of BPR can be seriously affected by an imbalanced data distribution because it tends to rank popular items ahead of personalized items. As a result, the personalized needs of users cannot be well met. In this paper, we propose a novel personalized recommendation method called similarity pairwise ranking (SPR) to rank users’ favorite items first. SPR eliminates the differences in the scores between popular and personalized items based on their similarity by using a new penalty. In such a way, the SPR-enhanced recommendation will render meaningful and personalized results that better meet the individual needs of users, and it overcomes the negative impact of imbalanced datasets. We design a model to illustrate the improvement of SPR: similarity pairwise ranking matrix factorization (SPRMF). Experimental results obtained using six datasets indicate the superiority in recommendation quality of SPRMF over the recent state-of-the-art methods.

Highlights

•

A pairwise method based on item similarity is proposed.

•

Item similarity is used to overcome the impact of data imbalance on the pairwise ranking method.

•

The method narrows the prediction score between similar item pairs in Bayesian personalized ranking method.

•

A uniform sample method based on ratings is used to obtain similar item pairs.

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The method is validated in six datasets with promising results.

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Cited By

Sun YLiu Q(2025)Collaborative filtering recommendation based on K-nearest neighbor and non-negative matrix factorization algorithmThe Journal of Supercomputing10.1007/s11227-024-06537-481:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06537-4
Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002

Index Terms

SPR: Similarity pairwise ranking for personalized recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 239, Issue C

Mar 2022

833 pages

ISSN:0950-7051

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 05 March 2022

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Citations

Cited By

Sun YLiu Q(2025)Collaborative filtering recommendation based on K-nearest neighbor and non-negative matrix factorization algorithmThe Journal of Supercomputing10.1007/s11227-024-06537-481:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06537-4
Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002

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