Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 192-198.doi: 10.11896/jsjkx.201000085

• Database & Big Data & Data Science • Previous Articles     Next Articles

Recommendation Algorithm Based on Knowledge Graph and Tag-aware

NING Ze-fei1, SUN Jing-yu2, WANG Xin-juan3   

  1. 1 College of Data Science,Taiyuan University of Technology,Taiyuan 030024,China
    2 College of Software,Taiyuan University of Technology,Taiyuan 030024,China
    3 College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China
  • Received:2020-10-16 Revised:2021-02-02 Online:2021-11-15 Published:2021-11-10
  • About author:NING Ze-fei,born in 1995,postgra-duate,is a member of China Computer Federation.His main research interests include recommendation system and data mining.
    SUN Jing-yu,born in 1975,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include collaborative web search,recommendation system and smart city.
  • Supported by:
    “1331 Project” of Shanxi Province(SC19100026),Key Research and Development Plan Project of the Shanxi Province Science and Technology Department(201803D31226) and Graduate Educational Reform Project of Shanxi Province(2019JG41).

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Abstract: Recommendation systems alleviate the problem of information overload caused by the rapid increase of data on the Internet.But traditional recommendation systems are not accurate enough due to data sparsity and cold start.Therefore,a novel recommendation algorithm based on knowledge graph and tag-aware (KGTA) is proposed.First,tags of items and users are used to capture low-order and high-order features through knowledge graph representation learning.The semantic information of entities and relationships in two knowledge graphs is embedded into a low-dimension vector space to obtain the unified representation of items and users.Then,deep neural networks and recurrent neural networks combining attention mechanism are respectively utilized to extract the latent features of items and users.Finally,ratings are predicted on the basis of latent features.KGTA not only takes relationship information and semantic information of knowledge graph and tags into consideration,but also learns latent features of items and users through deep structures.Experimental results on MovieLens datasets illustrate that the proposed algorithm performs better in rating prediction and improves the accuracy of recommendation.

Key words: Attention mechanism, Deep learning, Knowledge graph, Recommendation algorithm, Tag-aware

CLC Number: 

  • TP391
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