Neural User Preference Modeling Framework Based on Knowledge Graph
ZHU Guiming1,2, BIN Chenzhong2, GU Tianlong2, CHEN Wei2, JIA Zhonghao2
1.School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004
2.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004
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.
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2019, Vol. 32 
