基于图的混合推荐算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201704005

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Abstract Hybrid recommender is a significant way for solving the defect of various single recommender methods.A hybrid recommender algorithm based on graph is proposed in this paper.Various recommended factors are fused into graph to produce the final recommendation results. The similarity between items is calculated using the content attribute of recommended items to build correlation matrix of the nearest graph. The item profile is constructed according to the scored record of item to generate a vector function. Grounded on the above, a regular framework is used to build a graph-based learning model by combining correlation matrix and vector function and realize a personalization recommendation based on graph. By the experiments on MovieLens datasets and transaction data of Amazon online mart, the effectiveness of the proposed algorithm is verified.

Key words： Hybrid Recommender System Item Profile Item Content Attribute Graph User Rating

Received: 04 November 2016

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61603065), Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.kj1500920)

About author:: (ZHANG Yihao(Corresponding author), born in 1982, Ph.D., lecturer. His research interests include recommender system and machine learning.)
(LIU Xiaoyang, born in 1980, Ph. D., associate professor. His research interests include Chinese information processing.)
(LIU Wanping, born in 1986, Ph.D., associate professor. His research interests include complex network.)
(ZHU Changpeng, born in 1981, Ph.D., lecturer. His research interests include virtual machine.)

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	ZHANG Yihao
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	ZHU Changpeng

Cite this article:

ZHANG Yihao,LIU Xiaoyang,LIU Wanping等. Hybrid Recommender Algorithm Based on Graph[J]. , 2017, 30(4): 335-342.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/335

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