Deformable Convolutional Matrix Factorization for Document Context-Aware Recommendation in Social Networks

IEEE Access

Leyi Shi¹  Kai Lin²  Honglong Chen²  Jinnan Fu²  Shuai Wang³  Lei Zhang³  Lianhai Wang⁴ 

[1] College of Computer and Communication Engineering, China University of Petroleum, Qingdao, China;College of Information and Control Engineering, China University of Petroleum, Qingdao, China;Department of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China;Shandong Academy of Sciences, Qilu University of Technology, Jinan, China;

关键词: Collaborative filtering;    contextual information;    neural networks;    recommendation system;   

DOI  :  10.1109/ACCESS.2019.2917257

来源: DOAJ

【 摘 要 】

The extreme sparsity of the rating data seriously affects the recommendation quality of the recommendation system. In order to alleviate the problem of data sparsity, some convolutional neural network (CNN)-based models make full use of text data to improve the recommendation accuracy. However, due to the inherent properties of the traditional convolutional network, it can only extract features in a fixed position, and rely on the primitive bounding box based feature extraction, thus ignoring the flexibility of the traditional convolution. In this paper, we adopt a flexible convolutional network called deformable convolutional network (DCN), which extends the convolution transformation model capability by adding an offset layer to the traditional convolution layer, and then propose a novel deformable convolutional network matrix factorization (DCNMF) recommendation model. Specifically, we combine the DCN with word embedding to deeply capture the contextual information of document and build a latent model, which is incorporated into the probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct extensive experiments on the real-world datasets, and the experimental results show that the DCNMF outperforms the compared benchmarks.

【 授权许可】

Unknown