Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction-Reference-Cited by-同舟云学术

Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction

Published:2024-03-04 Issue: Volume:35 Page:510-519
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Ma Yunshan¹^ORCID,Liu Xiaohao²^ORCID,Wei Yinwei³^ORCID,Tao Zhulin⁴^ORCID,Wang Xiang⁵^ORCID,Chua Tat-Seng¹^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. University of Chinese Academy of Sciences, Beijing, China

3. Monash University, Melbourne, Australia

4. Communication University of China, Beijing, China

5. University of Science and Technology of China, Hefei, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635854

Reference55 articles.

1. Jinze Bai Chang Zhou Junshuai Song Xiaoru Qu Weiting An Zhao Li and Jun Gao. 2019. Personalized Bundle List Recommendation. In WWW. ACM 60--71.

2. Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. 2011. The Million Song Dataset. In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011).

3. Tzoof Avny Brosh, Amit Livne, Oren Sar Shalom, Bracha Shapira, and Mark Last. 2022. BRUCE: Bundle Recommendation Using Contextualized item Embeddings. In RecSys. ACM, 237--245.

4. Da Cao Liqiang Nie Xiangnan He Xiaochi Wei Shunzhi Zhu and Tat-Seng Chua. 2017. Embedding Factorization Models for Jointly Recommending Items and User Generated Lists. In SIGIR. ACM 585--594.

5. Bundle Recommendation and Generation with Graph Neural Networks

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