RecJPQ: Training Large-Catalogue Sequential Recommenders-Reference-Cited by-同舟云学术

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RecJPQ: Training Large-Catalogue Sequential Recommenders

Published:2024-03-04 Issue: Volume:33 Page:538-547
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
language:
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Author:

Petrov Aleksandr V.¹^ORCID,Macdonald Craig¹^ORCID

Affiliation:

1. University of Glasgow, Glasgow, United Kingdom

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635821

Reference65 articles.

1. 2023. Dimensionality Reduction - RDD-based API. https://spark.apache.org/docs/latest/mllib-dimensionality-reduction. [Online; accessed 14-December-2023].

2. 2023. How Many Videos Are on YouTube in 2024? https://earthweb.com/how-many-videos-are-on-youtube/. [Online; accessed 14-December-2023].

3. Jan Van Balen and Mark Levy. 2019. PQ-VAE: Efficient Recommendation Using Quantized Embeddings. In Proc. RecSys. 46--50.

4. Language Models Are Few-Shot Learners;Brown Tom;Proc. NeurIPS,2020

5. Christopher Burges. 2010. From RankNet to LambdaRank to LambdaMART: An overview. Learning 11 (2010).

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effective and Efficient Transformer Models for Sequential Recommendation;Proceedings of the 17th ACM International Conference on Web Search and Data Mining;2024-03-04

2. Transformers for Sequential Recommendation;Lecture Notes in Computer Science;2024

3. Effective and Efficient Transformer Models for Sequential Recommendation;Lecture Notes in Computer Science;2024

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