Scaling Sequential Recommendation Models with Transformers

Author:

Zivic Pablo1ORCID,Vazquez Hernan1ORCID,Sánchez Jorge2ORCID

Affiliation:

1. Mercado Libre Inc., Buenos Aires, Argentina

2. Mercado Libre Inc., Córdoba, Argentina

Publisher

ACM

Reference66 articles.

1. Ibrahim M. Alabdulmohsin Behnam Neyshabur and Xiaohua Zhai. 2022. Revisiting Neural Scaling Laws in Language and Vision. (2022).

2. Ibrahim M. Alabdulmohsin Xiaohua Zhai Alexander Kolesnikov and Lucas Beyer. 2023. Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design. (2023).

3. Newsha Ardalani, Carole-Jean Wu, Zeliang Chen, Bhargav Bhushanam, and Adnan Aziz. 2022. Understanding Scaling Laws for Recommendation Models. CoRR (2022). showeprint[arXiv]2208.08489

4. Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols

5. Yongjun Chen, Zhiwei Liu, Jia Li, Julian J. McAuley, and Caiming Xiong. 2022. Intent Contrastive Learning for Sequential Recommendation. In WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, Fré dé rique Laforest, Raphaë l Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Mé dini (Eds.). ACM, 2172--2182.

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