skip to main content
10.1145/3640457.3688100acmconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

The Role of Unknown Interactions in Implicit Matrix Factorization — A Probabilistic View

Published: 08 October 2024 Publication History

Abstract

Matrix factorization is a well-known and effective methodology for top-k list recommendation. It became widely known during the Netflix challenge in 2006, and since then, many adapted and improved versions have been published. A particularly interesting matrix factorization algorithm called iALS (for implicit Alternating Least Squares) adapts the method for implicit feedback, i.e. a setting where only a very small amount of positive labels are available along with a majority of unknown labels. Compared to the classical task of rating prediction, learning from implicit feedback is applicable to many more domains, as the data is more abundant and requires less effort to elicit from users. However, the sparsity, imbalance, and implicit nature of the signal also pose unique challenges to retrieving the most relevant items to recommend.
We revisit the role of unknown interactions in implicit matrix factorization. Traditionally, all unknowns are interpreted as negative samples and their importance in the training objective is then down-weighted to balance them out with the known, positive interactions. Interestingly, by adapting a probabilistic view of matrix factorization, we can retain the unknown nature of these interactions by modelling them as either positive or negative. With this new formulation that better fits the underlying data, we gain improved performance on the downstream recommendation task without any computational overhead compared to the popular iALS method.
This paper outlines the key insights needed to adapt iALS to use logistic regression. Furthermore, a logistic version of the popular full-rank EASE model is introduced in a similar fasion. An extensive experimental evaluation on several real-world datasets demonstrates the effectiveness of our approach. Additionally, a discrepancy between the need for weighting between factorization and autoencoder models is discovered, leading towards a better understanding of these methods.

Supplemental Material

PDF File - Poster Spotlight Slides
4 minute presentation with a high level overview of the paper.
PDF File - Poster
The conference poster

References

[1]
Charu C Aggarwal 2016. Recommender systems. Vol. 1. Springer.
[2]
Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017. A generic coordinate descent framework for learning from implicit feedback. In Proceedings of the 26th international conference on world wide web. 1341–1350.
[3]
Thierry Bertin-Mahieux, Daniel PW Ellis, Brian Whitman, and Paul Lamere. 2011. The million song dataset. (2011).
[4]
Christopher M Bishop. 1995. Neural networks for pattern recognition. Oxford university press.
[5]
Christopher Burges, Robert Ragno, and Quoc Le. 2006. Learning to rank with nonsmooth cost functions. Advances in neural information processing systems 19 (2006).
[6]
F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1–19.
[7]
Balázs Hidasi and Ádám Tibor Czapp. 2023. The effect of third party implementations on reproducibility. In Proceedings of the 17th ACM Conference on Recommender Systems. 272–282.
[8]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE international conference on data mining. Ieee, 263–272.
[9]
Ruoming Jin, Dong Li, Jing Gao, Zhi Liu, Li Chen, and Yang Zhou. 2021. Towards a better understanding of linear models for recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 776–785.
[10]
Christopher C Johnson 2014. Logistic matrix factorization for implicit feedback data. Advances in Neural Information Processing Systems 27, 78 (2014), 1–9.
[11]
Daeryong Kim and Bongwon Suh. 2019. Enhancing VAEs for collaborative filtering: flexible priors & gating mechanisms. In Proceedings of the 13th ACM conference on recommender systems. 403–407.
[12]
Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 2018 world wide web conference. 689–698.
[13]
Sam Lobel, Chunyuan Li, Jianfeng Gao, and Lawrence Carin. 2019. Towards amortized ranking-critical training for collaborative filtering. arXiv preprint arXiv:1906.04281 (2019).
[14]
Peter McCullagh. 2019. Generalized linear models. Routledge.
[15]
Andriy Mnih and Russ R Salakhutdinov. 2007. Probabilistic matrix factorization. Advances in neural information processing systems 20 (2007).
[16]
Xia Ning and George Karypis. 2011. Slim: Sparse linear methods for top-n recommender systems. In 2011 IEEE 11th international conference on data mining. IEEE, 497–506.
[17]
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In 2008 Eighth IEEE international conference on data mining. IEEE, 502–511.
[18]
István Pilászy, Dávid Zibriczky, and Domonkos Tikk. 2010. Fast als-based matrix factorization for explicit and implicit feedback datasets. In Proceedings of the fourth ACM conference on Recommender systems. 71–78.
[19]
Steffen Rendle, Walid Krichene, Li Zhang, and Yehuda Koren. 2021. IALS++: Speeding up matrix factorization with subspace optimization. arXiv preprint arXiv:2110.14044 (2021).
[20]
Steffen Rendle, Walid Krichene, Li Zhang, and Yehuda Koren. 2022. Revisiting the performance of ials on item recommendation benchmarks. In Proceedings of the 16th ACM Conference on Recommender Systems. 427–435.
[21]
Faisal Shehzad and Dietmar Jannach. 2023. Everyone’sa winner! on hyperparameter tuning of recommendation models. In Proceedings of the 17th ACM Conference on Recommender Systems. 652–657.
[22]
Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I Nikolenko. 2020. Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In Proceedings of the 13th international conference on web search and data mining. 528–536.
[23]
Harald Steck. 2015. Gaussian ranking by matrix factorization. In Proceedings of the 9th ACM Conference on Recommender Systems. 115–122.
[24]
Harald Steck. 2019. Embarrassingly shallow autoencoders for sparse data. In The World Wide Web Conference. 3251–3257.
[25]
Harald Steck, Maria Dimakopoulou, Nickolai Riabov, and Tony Jebara. 2020. Admm slim: Sparse recommendations for many users. In Proceedings of the 13th international conference on web search and data mining. 555–563.
[26]
Jason Weston, Samy Bengio, and Nicolas Usunier. 2011. Wsabie: Scaling up to large vocabulary image annotation. (2011).
[27]
Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the ninth ACM international conference on web search and data mining. 153–162.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2024

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. collaborative filtering
  2. implicit feedback
  3. matrix factorization
  4. recommender system

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

Conference

Acceptance Rates

Overall Acceptance Rate 85 of 414 submissions, 21%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 271
    Total Downloads
  • Downloads (Last 12 months)271
  • Downloads (Last 6 weeks)16
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media