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Title

Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender Systems.

Authors

Li, Hongchan; Zheng, Jinming; Jin, Baohua; Zhu, Haodong

Abstract

Knowledge graphs equipped with graph network networks (GNNs) have led to a successful step forward in alleviating cold start problems in recommender systems. However, the performance highly depends on precious high-quality knowledge graphs and supervised labels. This paper argues that existing knowledge-graph-based recommendation methods still suffer from insufficiently exploiting sparse information and the mismatch between personalized interests and general knowledge. This paper proposes a model named Adaptive Knowledge Contrastive Learning with Dynamic Attention (AKCL-DA) to address the above challenges. Specifically, instead of building contrastive views by randomly discarding information, in this study, an adaptive data augmentation method was designed to leverage sparse information effectively. Furthermore, a personalized dynamic attention network was proposed to capture knowledge-aware personalized behaviors by dynamically adjusting user attention, therefore alleviating the mismatch between personalized behavior and general knowledge. Extensive experiments on Yelp2018, LastFM, and MovieLens datasets show that AKCL-DA achieves a strong performance, improving the NDCG by 4.82%, 13.66%, and 4.41% compared to state-of-the-art models, respectively.

Subjects

KNOWLEDGE graphs; DATA augmentation; GRAPH neural networks; GRAPH labelings; ATTENTION

Publication

Electronics (2079-9292), 2024, Vol 13, Issue 18, p3594

ISSN

2079-9292

Publication type

Academic Journal

DOI

10.3390/electronics13183594

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