An efficient recommender system based on improved KGPolicy module

Wei Gao

doi:10.1117/12.2679222

25 May 2023 An efficient recommender system based on improved KGPolicy module

Wei Gao

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127121E (2023) https://doi.org/10.1117/12.2679222
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

As an appropriate method for mining negative signals from large-scale unobserved data, negative sampling plays a key role in recommender systems. Existing negative sampling methods, however, either static or adaptive ones, are insufficient to explicitly learn the structural representation information of user-item interactions. In this study, we hypothesize that indirect interactions between users and items, which enrich the structural representation information, could be useful to explicitly yield informative and high-quality negative samples. To fully exploit the indirect user-item interactions to generate negative samples, we construct a recommender system based on improved knowledge graph policy network (IKGPolicy), which works as a valid module to explore high-quality negatives. Specifically, by improving the graph learning module of the KGPolicy model, it is explicit to capture indirect and high-order interactive information, and yield a informative negative item to train the recommender. Experiments on the Amazon-Book, Last-FM, and Yelp2018 datasets show that our proposed method presents significant improvements over both negative sampling methods and knowledge graph-based models. Further analyses provide sufficient insights in capturing and learning the structural and interactive representation information.

Citation Download Citation

Wei Gao "An efficient recommender system based on improved KGPolicy module", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127121E (25 May 2023); https://doi.org/10.1117/12.2679222

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