Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230100057-7.doi: 10.11896/jsjkx.230100057

• Big Data & Data Science • Previous Articles     Next Articles

Knowledge Graph Recommendation Algorithm Combined with Graph Attention Mechanism

ZHANG Xiaowan1, DENG Qiujun2, LIU Xianhui2   

  1. 1 College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
    2 CAD Research Center,College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
  • Published:2023-11-09
  • About author:ZHANG Xiaowan,born in 1997,master candidate.Her main research interests include recommender systems and knowledge graph.
    LIU Xianhui,born in 1979,Ph.D,associate researcher,associate professor.His main research interests include machine learning,data mining and big data,networked manufacturing.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3305700).

PDF (PC)

986

Abstract: Due to the problems of data sparsity and cold start in traditional recommendation algorithms,and the item is regarded as a separate individual,the relationship between items is not considered.In order to solve these problems,recommender systems start to introduce auxiliary information.However,the existing path-based and embedding-based knowledge graph recommendation algorithms do not consider the importance of different entities to users,resulting in entities with lower importance having a greater impact on the recommendation results.Aiming at such limitations,this paper proposes a knowledge graph recommendation system combining graph attention mechanism,which firstly uses graph embedding method to generate initial representations of users and items,and then employs an attention mechanism to distinguish the importance of different neighbor entities during representation propagation,and generates user and item sums through weight summation.The final prediction layer generates the final representation of the user and item,and predicts the probability of user and item interaction based on the final representation.Compared with other algorithms on two public datasets Amazon-book and Last-fm,and experimental results show that the model has improved in indicators recall,ndcg,precision,HR,indicating that the model can effectively improve the accuracy of recommendation.

Key words: Recommendation system, Knowledge graph, Attention mechanism, Higher-order connectivity, Embedding propagation

CLC Number: 

  • TP391
[1]KHAN N,MA Z M,ULLAH A,et al.Categorization of know-ledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios:A comprehensive survey[J].Expert Systems with Applications,2022,206:117737.
[2]YU C L,WANG W Q,LIU X,et al.FolkScope:IntentionKnowledge Graph Construction for E-commerce Commonsense Discovery[J].arXiv:2211.08316,2023.
[3]BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:acollaboratively created graph database for structuring human knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data.ACM,2008:1247-1250.
[4]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.
[5]SUCHANEK F M,KASNECI G,WEIKUM G.Yago:a core of semantic knowledge[C]//Proceedings of the 16th International Conference on World Wide Web.ACM,2007:697-706.
[6]CARLSO N,BETTERIDGE J,WANG R C,et al.Coupled semi-supervised learning for information extraction[C]//Proceedings of the Third ACM International Conference on Web Search and Data Mining.ACM,2010:101-110.
[7]BELLEAU F,NOLIN M A,TOURIGNY N,et al.Bio2rdf:towards a mashup to build bioinformatics knowledge systems[J].Journal of Biomedical Informatics,2008,41(5):706-716.
[8]ZHOU L,GAO J F,LI D.The Design and Implementation ofXiaoIce,an Empathetic Social Chatbot[J].Computational Linguistics,2020,46(1):53-93.
[9]GAUR M,GUNARATNA K,SRINIVASAN V,et al.ISEEQ:Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022,36(10):10672-10680.
[10]WU L,HE X,WANG X,et al.A Survey on Accuracy-Oriented Neural Recommendation:From Collaborative Filtering to Information-Rich Recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2023,35(5):4425-4445.
[11]WANG X,HE X N,CAO Y X,et al.KGAT:Knowledge graph attention network for recommendation[C]//Proc of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM Press,2019:950-958.
[12]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].Advances in Neural Information Processing Systems,2017,30:5998-6008.
[13]TAN T Z,YAN J B.Hybrid recommendation system based on self- attention model[J].Computer Engineering and Applications,2020,56(13):172-180.
[14]ZHANG F Z,YUAN N J,LIAN D F,et al.Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM Press,2016:353-362.
[15]WANG H W,ZHANG F Z,XIE X,et al.DKN:Deep know-ledge-aware network for news recommendation[C]//The 27th International Conference on World Wide Web.ACM,2018:1835-1844.
[16]YU X,REN X,SUN Y Z,et al.Personalized entity recommendation:A heterogeneous information network approach[C]//Proceedings of the 7th ACM International Conference on Web Search and Data Mining.ACM,2014:283-292.
[17]ZHAO H,YAO Q M,LI J D,et al.Metagraph based recommen-dation fusion over heterogeneous information networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2017:635-644.
[18]WANG H W,ZHANG F Z,WANG J J,et al.Ripplenet:Propagating user preferences on the knowledge graph for recommender systems[C]//Proc of the 27th ACM International Conference on Information and Knowledge Management.ACM Press,2018:417-426.
[19]WANG H W,ZHAO M,XIE X,et al.Knowledge Graph Convolutional Networks for Recommender Systems[C]//The World Wide Web Conference.ACM Press,2019:3307-3313.
[20]LIU Y,YANG S,XU Y,et al.Contextualized Graph Attention Network for Recommendation With Item Knowledge Graph[J].IEEE Transactions on Knowledge and Data Engineering,2023,35(1):181-195.
[21]YUAN M,WAN J,WANG D.CRM-SBKG:Effective CitationRecommendation by Siamese BERT and Knowledge Graph[C]//International Conference on Electrical Engineering,Big Data and Algorithms(EEBDA).2023:909-914.
[22]LIN Y K,LIU Z Y,SUN M S,et al.Learning Entity and Relation Embeddings for Knowledge Graph Completion[C]//Twenty-ninth AAAI Conference on Artificial Intelligence.AAAI,2015:2181-2187.
[23]XU K,LI C T,TIAN Y L,et al.Representation Learning onGraphs with Jumping Knowledge Networks[C]//International Conference on Machine Learning.ACM,2018:5449-5458.
[24]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian Personalized Ranking from Implicit Feedback[C]//International Conference on Uncertainty in Artificial Intelligence.AUAI,2009:452-461.
[25]KINGMA D P,JIMMY B A.Adam:A Method for Stochastic Optimization[J].International Conference on Learning Representations.arXiv:1412.6980,2014.
[26]HE R N,MCAULEY J.Ups and Downs:Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering[C]//International World Wide Web Conferences.ACM,2016:507-517.
[27]HE X N A,LIAO L Z,ZHANG H W,et al.Neural Collaborative Filtering[C]//The World Wide Web Conference.ACM Press,2017:173-182.
[28]YANG J H,CHEN C M,WANG C J,et al.HOP-rec:high-order proximity for implicit recommendation[C]//Proceedings of the 12th ACM Conference on Recommender Systems.ACM,2018:140-144.
[29]HE X N,CHUA T S.Neural Factorization Machines for SparsePredictive Analytics[C]//International Conference on Research on Development in Information Retrieval.ACM,2017:355-364.
[30]AI Q Y,AZIZI V,CHEN X,et al.Learning HeterogeneousKnowledge Base Embeddings for Explainable Recommendation[J].Algorithms,2018,11(9):137.
[31]BORDES A,USUNIER N,GARCÍA-DURÁN A,et al.Translating Embeddings for Modeling Multi-relational Data[C]//NeurIPS.MIT Press,2013:2787-2795.
[32]VAN DEN BERG R,KIPF T N,WELLIN G.Graph Convolutional Matrix Completion[J].arXiv:1706.02263,2017.
[33]THOMAS N K,MAX W L.Semi-Supervised Classification with Graph Convolutional Networks.In ICLR[J].arXiv:1609.02907,2016.
[34]XAVIER G,YOSHUA B.Understanding the difficulty of training deep feedforward neural networks[C]//Artificial Intelligence and Statistics.JMLR,2010:249-256.
[1] WANG Jing, ZHANG Miao, LIU Yang, LI Haoling, LI Haotian, WANG Bailing, WEI Yuliang. Study on Dual-security Knowledge Graph for Process Industrial Control [J]. Computer Science, 2023, 50(9): 68-74.
[2] LI Haiming, ZHU Zhiheng, LIU Lei, GUO Chenkai. Multi-task Graph-embedding Deep Prediction Model for Mobile App Rating Recommendation [J]. Computer Science, 2023, 50(9): 160-167.
[3] ZHAI Lizhi, LI Ruixiang, YANG Jiabei, RAO Yuan, ZHANG Qitan, ZHOU Yun. Overview About Composite Semantic-based Event Graph Construction [J]. Computer Science, 2023, 50(9): 242-259.
[4] YI Liu, GENG Xinyu, BAI Jing. Hierarchical Multi-label Text Classification Algorithm Based on Parallel Convolutional Network Information Fusion [J]. Computer Science, 2023, 50(9): 278-286.
[5] LUO Yuanyuan, YANG Chunming, LI Bo, ZHANG Hui, ZHAO Xujian. Chinese Medical Named Entity Recognition Method Incorporating Machine ReadingComprehension [J]. Computer Science, 2023, 50(9): 287-294.
[6] LI Ke, YANG Ling, ZHAO Yanbo, CHEN Yonglong, LUO Shouxi. EGCN-CeDML:A Distributed Machine Learning Framework for Vehicle Driving Behavior Prediction [J]. Computer Science, 2023, 50(9): 318-330.
[7] ZHANG Yian, YANG Ying, REN Gang, WANG Gang. Study on Multimodal Online Reviews Helpfulness Prediction Based on Attention Mechanism [J]. Computer Science, 2023, 50(8): 37-44.
[8] TENG Sihang, WANG Lie, LI Ya. Non-autoregressive Transformer Chinese Speech Recognition Incorporating Pronunciation- Character Representation Conversion [J]. Computer Science, 2023, 50(8): 111-117.
[9] TANG Shaosai, SHEN Derong, KOU Yue, NIE Tiezheng. Link Prediction Model on Temporal Knowledge Graph Based on Bidirectionally Aggregating Neighborhoods and Global Aware [J]. Computer Science, 2023, 50(8): 177-183.
[10] WANG Jiahao, ZHONG Xin, LI Wenxiong, ZHAO Dexin. Human Activity Recognition with Meta-learning and Attention [J]. Computer Science, 2023, 50(8): 193-201.
[11] WANG Yu, WANG Zuchao, PAN Rui. Survey of DGA Domain Name Detection Based on Character Feature [J]. Computer Science, 2023, 50(8): 251-259.
[12] YAN Mingqiang, YU Pengfei, LI Haiyan, LI Hongsong. Arbitrary Image Style Transfer with Consistent Semantic Style [J]. Computer Science, 2023, 50(7): 129-136.
[13] MAO Huihui, ZHAO Xiaole, DU Shengdong, TENG Fei, LI Tianrui. Short-term Subway Passenger Flow Forecasting Based on Graphical Embedding of Temporal Knowledge [J]. Computer Science, 2023, 50(7): 213-220.
[14] ZHANG Shunyao, LI Huawang, ZHANG Yonghe, WANG Xinyu, DING Guopeng. Image Retrieval Based on Independent Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220300092-6.
[15] LIU Haowei, YAO Jingchi, LIU Bo, BI Xiuli, XIAO Bin. Two-stage Method for Restoration of Heritage Images Based on Muti-scale Attention Mechanism [J]. Computer Science, 2023, 50(6A): 220600129-8.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.