Multi-behavior recommendation algorithm based on multi-view item semantic learning

Bo Zhang; Peng Zhao; Zhaoju Zeng

doi:10.1117/12.2656684

20 October 2022 Multi-behavior recommendation algorithm based on multi-view item semantic learning

Bo Zhang, Peng Zhao, Zhaoju Zeng

Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124513P (2022) https://doi.org/10.1117/12.2656684
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China

Abstract

Multi-behavior recommender systems model and learn user preferences by using users' multiple behavioral interbehavior. However, the existing multi-behavior recommendation models usually ignore the independent features of each independent behavior and the personalized interbehavior relationship of users, and cannot fully model the item features. To this end, this paper proposes a multi-behavior recommendation algorithm based on multi-view item semantic learning. Graph neural network is used to mine item semantics from the perspective of independent behavior, and multi-head attention mechanism is used to learn the correlation between different behavior under the graph learning paradigm. At the same time, iterative propagation captures the collaborative signals in the user's multi-behavior information, and learns item semantic information from multiple perspectives. Compared with existing multi-behavior recommendation algorithms on Yelp and BeiBei datasets, considering item multi-view semantic learning can effectively improve recommendation accuracy.

Citation Download Citation

Bo Zhang, Peng Zhao, and Zhaoju Zeng "Multi-behavior recommendation algorithm based on multi-view item semantic learning", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124513P (20 October 2022); https://doi.org/10.1117/12.2656684

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