针对推荐系统的动态关注自适应知识对比学习

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongchan Li, Jinming Zheng, Baohua Jin, Haodong Zhu
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引用次数: 0

摘要

配备图网络(GNN)的知识图谱在缓解推荐系统的冷启动问题方面取得了成功。然而,其性能高度依赖于珍贵的高质量知识图谱和监督标签。本文认为,现有的基于知识图谱的推荐方法仍然存在对稀疏信息利用不足以及个性化兴趣与一般知识不匹配的问题。本文提出了一种名为 "具有动态注意力的自适应知识对比学习"(AKCL-DA)的模型来应对上述挑战。具体来说,本研究设计了一种自适应数据增强方法,以有效利用稀疏信息,而不是通过随机丢弃信息来建立对比视图。此外,本研究还提出了一种个性化动态注意力网络,通过动态调整用户注意力来捕捉知识感知的个性化行为,从而缓解个性化行为与一般知识之间的不匹配问题。在 Yelp2018、LastFM 和 MovieLens 数据集上进行的大量实验表明,AKCL-DA 性能强劲,与最先进的模型相比,NDCG 分别提高了 4.82%、13.66% 和 4.41%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Knowledge Contrastive Learning with Dynamic Attention for Recommender Systems
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.
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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