基于知识图谱的用户偏好神经建模框架

doi:10.16451/j.cnki.issn1003-6059.201907010

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Abstract

An end-to-end neural user preference modeling framework incorporating knowledge graph into recommender systems, neural user preference modeling framework based on knowledge graph(NUPM), is proposed aiming at the limitations of the current feature-based and path-based knowledge aware recommendation method. Historical interaction items of users in knowledge graph are considered as preference origin of NUPM. Then, potential preferences of users are learned by propagating user interests through relational links between entities in knowledge graph. Furthermore, an attention network is exploited to combine the preference features of different propagation stages to construct final user preference vector. The experimental results on real dataset show the effectiveness of NUPM in personalized recommendation for characterizing user preference.

Key words： Recommender System Knowledge Graph Preference Propagation Attention Mechanism

Received: 17 April 2019

ZTFLH:

TP 311

Fund:

Supported by National Natural Science Foundation of China(No.U1711263,U1501252,61572146), Natural Science Foundation of Guangxi Province(No.2016GXNSFDA380006, AC16380122), Innovation-Driven Major Projects of Guangxi Province(No.AA17202024), Guangxi Information Science Experiment Center Platform Construction Project(No.PT1601), Basic Ability Promotion Project for Young and Middle-aged Teachers in Universities of Guangxi(No.2018KY0203)

About author:: ZHU Guiming, master student. His research interests include machine learning, data mining and recommender system.BIN Chenzhong(Corresponding author), Ph.D. candidate, lecturer. His research interests include data mining and intelligent re-commendation.GU Tianlong, Ph.D., professor. His research interests include knowledge engineering and symbolic reasoning.CHEN Wei, master student. His research interests include machine learning, data mi-ning and recommender system.JIA Zhonghao, master student. His research interests include machine learning, data mining and recommender system.)

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	ZHU Guiming
	BIN Chenzhong
	GU Tianlong
	CHEN Wei
	JIA Zhonghao

Cite this article:

ZHU Guiming,BIN Chenzhong,GU Tianlong等. Neural User Preference Modeling Framework Based on Knowledge Graph[J]. , 2019, 32(7): 661-668.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/661

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