Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization-Reference-Cited by-同舟云学术

Achieving a Better Tradeoff in Multi-stage Recommender Systems through Personalization

Published:2024-08-24 Issue: Volume:6 Page:4939-4950
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Evnine Ariel¹^ORCID,Ioannidis Stratis²^ORCID,Kalimeris Dimitris¹^ORCID,Kalyanaraman Shankar¹^ORCID,Li Weiwei¹^ORCID,Nir Israel¹^ORCID,Sun Wei¹^ORCID,Weinsberg Udi¹^ORCID

Affiliation:

1. Meta, Menlo Park, CA, USA

2. Northeastern University, Boston, MA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671593

Reference51 articles.

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3. Rohan Anil Sandra Gadanho Da Huang Nijith Jacob Zhuoshu Li Dong Lin Todd Phillips Cristina Pop Kevin Regan Gil I Shamir et al. 2022. On the factory floor: ML engineering for industrial-scale ads recommendation models. arXiv preprint arXiv:2209.05310 (2022).

4. Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures

5. An Bian, Kfir Levy, Andreas Krause, and Joachim M Buhmann. 2017. Continuous DR-submodular maximization: Structure and algorithms. Advances in Neural Information Processing Systems 30 (2017).