基于深度神经网络和加权隐反馈的个性化推荐

doi:10.16451/j.cnki.issn1003-6059.202004002

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Abstract

In singular value decomposition++(SVD++), inner product of user and item feature vector is regarded as user's rating of items. However, inner product cannot capture the high-order nonlinear relationship between the user and the item. In addition, the contribution of different interactive items cannot be distinguished when user's implicit feedback is incorporated in SVD++. A recommendation algorithm based on deep neural network and weighted implicit feedback is proposed to solve the two problems. Deep neural network is adopted to model the relationship between the user and the object and attention mechanism is utilized to calculate the weight of historical interactive items in modeling user's implicit feedback. Experiments on public datasets verify the effectiveness of the proposed algorithm.

Key words： Recommendation System Collaborative Filtering Weighted Implicit Feedback Matrix Factorization

Received: 29 September 2019

ZTFLH:

TP 391.3

Fund:

Supported by National Key Research and Development Program of China(No.2017YFB0803301), National Natural Science Foundation of China(No.61772170)

Corresponding Authors: XUE Feng, Ph.D., professor. His research interests include information retrieval and multimedia analysis.

About author:: XUE FengCorresponding author, Ph.D., professor. His research interests include information retrieval and multimedia analysis.

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	XUE Feng
	LIU Kai
	WANG Dong
	ZHANG Haobo

Cite this article:

XUE Feng,LIU Kai,WANG Dong等. Personalized Recommendation Algorithm Based on Deep Neural Network and Weighted Implicit Feedback[J]. , 2020, 33(4): 295-302.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202004002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I4/295

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