计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 192-198.doi: 10.11896/jsjkx.201000085

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于知识图谱和标签感知的推荐算法

宁泽飞1, 孙静宇2, 王欣娟3   

  1. 1 太原理工大学大数据学院 太原030024
    2 太原理工大学软件学院 太原030024
    3 太原理工大学信息与计算机学院 太原030024
  • 收稿日期:2020-10-16 修回日期:2021-02-02 出版日期:2021-11-15 发布日期:2021-11-10
  • 通讯作者: 孙静宇(whitesunpersun@163.com)
  • 作者简介:ningzefei@163.com
  • 基金资助:
    山西省“1331工程”项目(SC19100026);山西省科技厅重点研发计划项目(201803D31226);山西省研究生教改项目(2019JG41)

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).

摘要: 推荐系统缓解了互联网数据量剧增带来的信息过载问题,但传统的推荐系统由于数据稀疏和冷启动等问题导致推荐算法的准确性不高。因此,文中提出了一种基于知识图谱和标签感知的推荐算法(Knowledge Graph and Tag-Aware,KGTA)。首先,利用项目和用户标签信息,通过知识图谱表示学习捕获低阶与高阶特征,将两个知识图谱中实体和关系的语义信息嵌入低维的向量空间中,从而获得项目和用户的统一表示。其次,分别利用深度神经网络和加入注意力机制的递归神经网络来提取项目和用户的潜在特征。最后,根据潜在特征预测评分。该算法不仅利用了知识图谱和标签的关系信息和语义信息,而且通过深层结构学习了项目和用户的隐含特征。在MovieLens数据集上的实验结果表明,该算法能够有效预测用户评分,提高推荐结果的准确性。

关键词: 标签感知, 深度学习, 推荐算法, 知识图谱, 注意力机制

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

中图分类号: 

  • TP391
[1]DAVID R,JOHN G,JOHN R.Data age 2025:the digitization of the world from edge to core[EB/OL].[2019-05-12].https://www.seagate.com/cn/zh/our-story/data-age-2025.
[2]HUANG L W,JIANG B T,LU S Y,et al.Review of recommendation systems based on deep learning[J].Chinese Journal of Computers,2018,41(7):1619-1647.
[3]BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recom-mender systems survey[J].Knowledge-Based Systems,2013,46:109-132.
[4]INUZUKA K,HAYASHI T,TAKAGI T.RecommendationSystem Based on Prediction of User Preference Changes[C]//2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).IEEE,2016:192-199.
[5]SHEN J,QIAO S J,HAN N,et al.Personalized Recommendation Model with Multiple Information Fusion[J].Journal of Chongqing University of Technology (Natural Science),2021,35(3):128-138.
[6]XIANG L.Practice of Recommendation System[M].Posts &Telecom Press,2012.
[7]LIU Q,LI Y,DUAN H,et al.Knowledge Graph Construction Techniques[J].Journal of Computer Research and Development,2016,53(3):582-600.
[8]LEHMANN J,ISELE R,JAKOB M,et al.Dbpedia-a large-scale,multilingual knowledge base extracted from wikipedia[J].Semantic web,2015,6(2):167-195.
[9]SPEER R,CHIN J,HAVASI C.ConceptNet 5.5:An OpenMultilingual Graph of General Knowledge[C]//Thirty-first AAAI conference on artificial intelligence.2017:4444-4451.
[10]BERANT J,CHOU A,FROSTIG R,et al.Semantic parsing on freebase from question-answer pairs[C]//Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing.2013:1533-1544.
[11]VRANDEČIĆ D,KRÖTZSCH M.Wikidata:A Free Collabora-tive Knowledgebase[J].Communications of the ACM,2014,57(10):78-85.
[12]FU W B,SUN T,LIANG J,et al.Review of Principle and Application of Deep Learning[J].Computer Science,2018,45(S1):11-15.
[13]DA'U A,SALIM N.Recommendation system based on deeplearning methods:a systematic review and new directions[J].Artificial Intelligence Review,2020,53(4):2709-2748.
[14]HINTON G E,SALAKHUTDINOV R R.Reducing the Dimensionality of Data with Neural Networks[J].Science,2006,313(5786):504-507.
[15]COVINGTON P,ADAMS J,SARGIN E.Deep Neural Net-works for YouTube Recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:191-198.
[16]ZHANG L,LUO T,ZHANGA F,et al.A RecommendationModel Based on Deep Neural Network[J].IEEE Access,2018,6:9454-9463.
[17]RUMELHART D,HINTON G,WILLIAMS R.Learning Representations by Back Propagating Errors[J].Nature,1986,323(6088):533-536.
[18]ZHOU Y,HUANG C,HU Q,et al.Personalized learning full-path recommendation model based on LSTM neural networks[J].Information Sciences,2018,444:135-152.
[19]YANG L,ZHENG Y,CAI X,et al.A LSTM based Model for Personalized Context-Aware Citation Recommendation[J].IEEE Access,2018,6:59618-59627.
[20]LIANG H,XU Y,LI Y,et al.Connecting users and items with weighted tags for personalized item recommendations[C]//Proceedings of the 21st ACM conference on Hypertext and hypermedia.2010:51-60.
[21]WU Y,YAO Y,XU F,et al.Tag2Word:Using Tags to Gene-rate Words for Content Based Tag Recommendation[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management.2016:2287-2292.
[22]ZUO Y,ZENG J,GONG M,et al.Tag-Aware RecommenderSystems Based on Deep Neural Networks[J].Neurocomputing,2016,204:51-60.
[23]XU Z,LUKASIEWICZ T,CHEN C,et al.Tag-Aware Persona-lized Recommendation Using a Hybrid Deep Model[C]//International Joint Conferences on Artificial Intelligence.AAAI Press,2017:3196-3202.
[24]LIANG N,ZHENG H,CHEN J,et al.TRSDL:Tag-Aware Re-commender System Based on Deep Learning-Intelligent Computing Systems[J].Applied Sciences,2018,8(5):799.
[25]WANG H,ZHANG F,WANG J,et al.Ripple Network:Propagating User Preferences on the Knowledge Graph for Recommender Systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.2018:417-426.
[26]WANG H,ZHAO M,XIE X,et al.Knowledge Graph Convolutional Networks for Recommender Systems[C]//The World Wide Web Conference (WWW'19).2019:3307-3313.
[27]WANG H,ZHANG F,XIE X,et al.DKN:Deep Knowledge-Aware Network for News Recommendation[C]//Proceedings of the 2018 world wide web conference.2018:1835-1844.
[28]SUN Z,YANG J,ZHANG J,et al.Recurrent Knowledge Graph Embedding for Effective Recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems.2018:297-305.
[29]SANG L,XU M,QIAN S,et al.Knowledge graph enhancedneural collaborative recommendation[J].Expert Systems with Applications,2020,164:113992.
[30]YANG Z,DONG S.HAGERec:Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation[J].Knowledge-Based Systems,2020,204:106194.
[31]SIL A,YATES A.Re-Ranking for Joint Named-Entity Recognition and Linking[C]//Proceedings of the 22nd ACM International Conference on Information & Knowledge Management.2013:2369-2374.
[32]ZHANG F,YUAN N J,LIAN D,et al.Collaborative Knowledge Base Embedding for Recommender Systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:353-362.
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