Session-based recommendation by exploiting substitutable and complementary relationships from multi-behavior data-Reference-Cited by-同舟云学术

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Session-based recommendation by exploiting substitutable and complementary relationships from multi-behavior data

Published:2023-12-26 Issue: Volume: Page:
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Wu Huizi,Geng Cong,Fang Hui^ORCID

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Shanghai Rising-Star Program

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10618-023-00994-w.pdf

Reference48 articles.

1. Chen T, Wong RC-W (2020) Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1172–1180

2. Chen S, Moore JL, Turnbull D., Joachims T (2012) Playlist prediction via metric embedding. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 714–722

3. Chen T, Yin H, Ye G, Huang Z, Wang Y, Wang M (2020) Try this instead: personalized and interpretable substitute recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 891–900

4. Donkers T, Loepp B, Ziegler J (2017) Sequential user-based recurrent neural network recommendations. In: Proceedings of the 11th ACM conference on recommender systems, pp 152–160

5. Fang H, Zhang D, Shu Y, Guo G (2020) Deep learning for sequential recommendation: algorithms, influential factors, and evaluations. ACM Trans Inf Syst (TOIS) 39(1):1–42

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