On Reducing User Interaction Data for Personalization-Reference-Cited by-同舟云学术

On Reducing User Interaction Data for Personalization

Published:2023-06-23 Issue: Volume: Page:
ISSN:2770-6699
Container-title:ACM Transactions on Recommender Systems
language:en
Short-container-title:ACM Trans. Recomm. Syst.

Author:

Rendle Steffen¹,Zhang Li¹

Affiliation:

1. Google Research

Abstract

Most recommender systems rely on user interaction data for personalization. Usually, the recommendation quality improves with more data. In this work, we study the quality implications when limiting user interaction data for personalization purposes. We formalize this problem and provide algorithms for selecting a smaller subset of user interaction data. We propose a selection method that picks the subset of a user’s history items that maximizes the expected recommendation quality. We show on well studied benchmarks that it is possible to achieve high quality results with small subsets of less than ten items per user.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3600097

Reference39 articles.

1. Mehdi Allahyari Seyedamin Pouriyeh Mehdi Assefi Saeid Safaei Elizabeth D Trippe Juan B Gutierrez and Krys Kochut. 2017. Text summarization techniques: a brief survey. arXiv preprint arXiv:1707.02268(2017). Mehdi Allahyari Seyedamin Pouriyeh Mehdi Assefi Saeid Safaei Elizabeth D Trippe Juan B Gutierrez and Krys Kochut. 2017. Text summarization techniques: a brief survey. arXiv preprint arXiv:1707.02268(2017).

2. Oren Anava , Shahar Golan , Nadav Golbandi , Zohar Karnin , Ronny Lempel , Oleg Rokhlenko , and Oren Somekh . 2015 . Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design . In Proceedings of the 24th International Conference on World Wide Web (WWW ’15). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 45–54 . https://doi.org/10.1145/2736277.2741109 10.1145/2736277.2741109 Oren Anava, Shahar Golan, Nadav Golbandi, Zohar Karnin, Ronny Lempel, Oleg Rokhlenko, and Oren Somekh. 2015. Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design. In Proceedings of the 24th International Conference on World Wide Web (WWW ’15). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 45–54. https://doi.org/10.1145/2736277.2741109

3. Thierry Bertin-Mahieux , Daniel P. W. Ellis , Brian Whitman , and Paul Lamere . 2011 . The million song dataset . In In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR). Thierry Bertin-Mahieux, Daniel P. W. Ellis, Brian Whitman, and Paul Lamere. 2011. The million song dataset. In In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR).

4. Operationalizing the Legal Principle of Data Minimization for Personalization

5. Steve Chien , Prateek Jain , Walid Krichene , Steffen Rendle , Shuang Song , Abhradeep Thakurta , and Li Zhang . 2021 . Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates . In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research), Marina Meila and Tong Zhang (Eds.), Vol. 139 . PMLR, 1877–1887. https://proceedings.mlr.press/v139/chien21a.html Steve Chien, Prateek Jain, Walid Krichene, Steffen Rendle, Shuang Song, Abhradeep Thakurta, and Li Zhang. 2021. Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research), Marina Meila and Tong Zhang (Eds.), Vol. 139. PMLR, 1877–1887. https://proceedings.mlr.press/v139/chien21a.html