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Abstract：
To solve the problem of low recommendation accuracy caused by too little user behavior information in the current behavior recommendation system, an algorithm based on end-to-end data enhancement was proposed. In this paper, knowledge graph is constructed by learning and integrating structured knowledge network. Moreover, the characteristics of users with high preference similarity can be propagated through the inter-entity relations mapped by the knowledge map to reconstruct the preference vector of users. Through comparative experiments on open data sets, the AUC of RNN model, CNN model, RNN attention model and ATRank were improved by 3.28%, 2.35%, 2.89% and 1.30%, respectively. © 2019 IEEE.
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Year： 2019
Page： 1359-1364
Language： English
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WoS CC Cited Count： 0
SCOPUS Cited Count： 1
ESI Highly Cited Papers on the List： 0 Unfold All
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30 Days PV： 6
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