1. 基于动态二分网络表示学习的推荐方法.
- Author
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张阳阳, 陈可佳, and 张 杰
- Subjects
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VECTOR spaces , *BIPARTITE graphs , *INFORMATION services , *RECOMMENDER systems , *HETEROGENEITY - Abstract
Representation learning of a user-item interaction network becomes an effective recommendation method. Most of the existing methods regard the interaction network as a static homogeneous network, ignoring the impact of interaction timing and node heterogeneity. In response to this problem, this paper proposed a recommendation method based on dynamic bipartite network representation learning. Firstly, the method constructed a time-series weighted bipartite network, and then respectively mapped user nodes and item nodes to different vector spaces to preserve the heterogeneity of the network, and aggregated the first-order and high-order neighbor information for center nodes with graph convolution. Finally it used a multi-layer perceptron to learn the nonlinear relationship between the two types of node embeddings and performed top-N recommendation. Experimental results on Amazon and Taobao datasets show that the proposed method is significantly superior than the related methods based on static or heterogeneous network representation learning in HR and NDCG indicators. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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