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One-class Matrix Factorization: Point-Wise Regression-Based or Pair-Wise Ranking-Based?

Published: 08 October 2024 Publication History

Abstract

One-class matrix factorization (MF) is an important technique for recommender systems with implicit feedback. In one widely used setting, a regression function is fit in a point-wise manner on observed and some unobserved (user, item) entries. Recently, in AAAI 2019, Chen et al. [2] proposed a pair-wise ranking-based approach for observed (user, item) entries to be compared against unobserved ones. They concluded that the pair-wise setting performs consistently better than the more traditional point-wise setting. However, after some detailed investigation, we explain by mathematical derivations that their method may perform only similar to the point-wise ones. We also identified some problems when reproducing their experimental results. After considering suitable settings, we rigorously compare point-wise and pair-wise one-class MFs, and show that the pair-wise method is actually not better. Therefore, for one-class MF, the more traditional and mature point-wise setting should still be considered. Our findings contradict the conclusions in [2] and serve as a call for caution when researchers are comparing between two machine learning methods.

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Files include "supplementary materials of the main paper", "Presentation video (2min)", and the "slides of the video."
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Files include "supplementary materials of the main paper", "Presentation video (2min)", and the "slides of the video."

References

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Olivier Chapelle and Yi Chang. 2011. Yahoo! Learning to Rank Challenge Overview. In JMLR Workshop and Conference Proceedings: Workshop on Yahoo! Learning to Rank Challenge, Vol. 14. 1–24.
[2]
Jin Chen, Defu Lian, and Kai Zheng. 2019. Improving one-class collaborative filtering via ranking-based implicit regularizer. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 37–44.
[3]
Lars Eldén and Haesun Park. 1999. A Procrustes problem on the Stiefel manifold. Numer. Math. 82, 4 (1999), 599–619.
[4]
Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web (WWW). 507–517.
[5]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 263–272.
[6]
Noam Koenigstein and Ulrich Paquet. 2013. Xbox movies recommendations: Variational Bayes matrix factorization with embedded feature selection. In Proceedings of the 7th ACM Conference on Recommender Systems. 129–136.
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Yanen Li, Jia Hu, Chengxiang Zhai, and Ye Chen. 2010. Improving one-class collaborative filtering by incorporating rich user information. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM). 959–968.
[8]
Defu Lian, Xing Xie, and Enhong Chen. 2019. Discrete matrix factorization and extension for fast item recommendation. IEEE Transactions on Knowledge and Data Engineering 33, 5 (2019), 1919–1933.
[9]
Rong Pan and Martin Scholz. 2009. Mind the Gaps: Weighting the Unknown in Large-scale One-class Collaborative Filtering. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). 667–676.
[10]
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In IEEE International Conference on Data Mining (ICDM). 502–511.
[11]
Weike Pan and Li Chen. 2013. GBPR: Group preference based Bayesian personalized ranking for one-class collaborative filtering. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI). 2691–2697.
[12]
Ulrich Paquet and Noam Koenigstein. 2013. One-class collaborative filtering with random graphs. In Proceedings of the 22nd International Conference on World Wide Web. 999–1008.
[13]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI). 452–461.
[14]
Hsiang-Fu Yu, Mikhail Bilenko, and Chih-Jen Lin. 2017. Selection of Negative Samples for One-class Matrix Factorization. In Proceedings of SIAM International Conference on Data Mining (SDM). http://www.csie.ntu.edu.tw/ cjlin/papers/one-class-mf/biased-mf-sdm-with-supp.pdf

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
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Published: 08 October 2024

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Author Tags

  1. One-Class Matrix Factorization
  2. Pair-Wise Loss
  3. Point-Wise Loss
  4. Recommender Systems
  5. Reproducibility

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  • Refereed limited

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  • National Science and Technology Council of Taiwan

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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