Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective-Reference-Cited by-同舟云学术

Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective

Published:2023-10-21 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Quan Shigang¹^ORCID,Liu Shui²^ORCID,Zheng Zhenzhe¹^ORCID,Wu Fan¹^ORCID

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

2. Alibaba Group, Hangzhou, China

Funder

National Key R&D Program of China No. 2020YFB1707900; China NSF grant No. 62132018, U2268204, 62272307?61902248, 61972254, 61972252, 62025204, 62072303; Shanghai Science and Technology fund 20PJ1407900; Alibaba Group through Alibaba Innovative Research Program; Tencent Rhino Bird Key Research Project

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614866

Reference38 articles.

1. The dynamics of repeat consumption

2. ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping

3. Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2015 . Neural Machine Translation by Jointly Learning to Align and Translate . In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.).

4. Modeling User Consumption Sequences

5. Buy It Again

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