Probabilistic Attention for Sequential Recommendation-Reference-Cited by-同舟云学术

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Probabilistic Attention for Sequential Recommendation

Published:2024-08-24 Issue: Volume:19 Page:1956-1967
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
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Author:

Liu Yuli¹^ORCID,Walder Christian²^ORCID,Xie Lexing³^ORCID,Liu Yiqun⁴^ORCID

Affiliation:

1. Qinghai University & Qinghai Provincial Key Laboratory of Media Integration Technology and Communication, Xining, China

2. Google Research, Brain Team, Montreal, Canada

3. Australian National University & Data61 CSIRO, Canberra, Australia

4. Department of Computer Science and Technology, Tsinghua University & Zhongguancun Laboratory, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671733

Reference67 articles.

1. Samira Abnar and Willem Zuidema. 2020. Quantifying attention flow in transformers. arXiv preprint arXiv:2005.00928 (2020).

2. Personalized Bundle List Recommendation

3. Latent Cross

4. Category-aware Collaborative Sequential Recommendation

5. Laming Chen, Guoxin Zhang, and Eric Zhou. 2018. Fast greedy map inference for determinantal point process to improve recommendation diversity. Advances in Neural Information Processing Systems 31 (2018).

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