HetNERec: Heterogeneous network embedding based recommendation

被引:56
|
作者
Zhao, Zhongying [1 ]
Zhang, Xuejian [1 ]
Zhou, Hui [1 ]
Li, Chao [1 ]
Gong, Maoguo [1 ,2 ]
Wang, Yongqing [3 ]
机构
[1] Shandong Univ Sci & Technol, Sch Comp Sci & Engn, Qingdao 266590, Peoples R China
[2] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China
[3] Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China
基金
中国国家自然科学基金;
关键词
Heterogeneous network; Network embedding; Recommender system; Heterogeneous network embedding; FACTORIZATION; COMMUNITY;
D O I
10.1016/j.knosys.2020.106218
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Traditional recommendation techniques are hindered by the simplicity and sparsity of user-item interaction data and can be improved by introducing auxiliary information related to users and/or items. However, most studies have focused on a single typed external relationship and not fully utilized the latent relationships among users and items. In this paper, we propose a heterogeneous network embedding-based recommendation method called HetNERec. Specifically, we first construct the co-occurrence networks by extracting multiple co-occurrence relationships from a recommendation-oriented heterogeneous network. We then propose an integration function to integrate multiple network embedded representations into a single representation to enhance the recommendation performance. Finally, the matrix factorization is extended by integrating the embedded representations and considering the latent relationships among users and items. The experimental results on real-world datasets demonstrate that the proposed HetNERec outperforms several state-of-the-art recommendation methods. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
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