Sequence Data Enhancement Method Based on Knowledge Graph

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 knowl...

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Bibliographic Details
Published in2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) pp. 1359 - 1364
Main Authors Xie, Huosheng, Chai, Wenda, Lin, Shufeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2019
Subjects
6G mobile communication
Big Data
Channel hot electron injection
Cloud computing
Distributed processing
Enhancement
Inter-entity
Knowledge Graph
Recommender System
Social computing
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Summary: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.
DOI:10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00195