基于信任和不信任关系的实值受限玻尔兹曼机推荐算法
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摘要
在社交网络与电子商务快速融合的背景下,将基于信任关系的推荐技术应用于电子商务领域实现个性化推荐已得到广泛研究.现有推荐算法鲜有考虑用户间不信任效应,导致社交信任度量过于保守,较大地影响了推荐系统准确性.针对现有推荐算法忽视不信任关系导致的非对称效应缺陷,本文提出一种结合信任和不信任的实值受限玻尔兹曼机推荐算法(TDA-RBM),首先建立个人受限玻尔兹曼机,进而运用用户社交行为特征信息分析用户信任与不信任关系并进行度量,在此基础上构造信任-不信任监督机制并用于TDA-RBM方法的优化,同时对该方法的有效性进行分析.通过Epinions数据进行的对比实验表明了TDA-RBM方法的有效性以及不信任关系的引入能有效提高推荐准确性.
The recommendation technology based on trust relationship is applied in.the field of mobile e-commerce,and the use of social trust network to realize personalized recommendation has been widely studied.The inaccurate measurement of social trust has great influence on the accuracy of recommendation system.The paper is concerned with the problem that trust mechanism is ignored the asymmetric effect brought by the distrust relationship,and then restricts the accurate measurement of social trust.A novel algorithm of recommendation is proposed which is a real-value constrained Boltzmann machine recommendation algorithm based on trust-distrust relationship(TDA-RBM),establish restricted Boltzmann machine for each user,using normalized real value analysis and training process of RBM.Construct trust-distrust supervision mechanism,trust,distrust and the trust transfer are introduced,and the trust as weights for the TDA-RBM prediction score.Finally,we experimented on Epinions datasets and the comparison experiment proves that the TDA-RBM algorithm can effectively improve the accuracy of recommendation,which shows the effectiveness and excellency of the algorithm.
The recommendation technology based on trust relationship is applied in.the field of mobile e-commerce,and the use of social trust network to realize personalized recommendation has been widely studied.The inaccurate measurement of social trust has great influence on the accuracy of recommendation system.The paper is concerned with the problem that trust mechanism is ignored the asymmetric effect brought by the distrust relationship,and then restricts the accurate measurement of social trust.A novel algorithm of recommendation is proposed which is a real-value constrained Boltzmann machine recommendation algorithm based on trust-distrust relationship(TDA-RBM),establish restricted Boltzmann machine for each user,using normalized real value analysis and training process of RBM.Construct trust-distrust supervision mechanism,trust,distrust and the trust transfer are introduced,and the trust as weights for the TDA-RBM prediction score.Finally,we experimented on Epinions datasets and the comparison experiment proves that the TDA-RBM algorithm can effectively improve the accuracy of recommendation,which shows the effectiveness and excellency of the algorithm.
引文
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[21]Deng S,Huang L,Xu G X,et al.On deep learning for trust-aware recommendations in social networks[J].IEEE Transactions on Neural Networks&Learning Systems,2017,28(9):1164-1177.
[22]Alonso J,Paredes R,Rosso P.Data mapping by restricted Boltzmann machines for social circles detection[C]//2015 International Joint Conference on Neural Networks,2015:1-7.
[23]Ma X,Lu H,Gan Z,et al.An explicit trust and distrust clustering based collaborative filtering recommendation approach[J].Electronic Commerce Research&Applications,2017,25(1):29-39.
[24]Rafailidis D,Crestani F.A regularization method with inference of trust and distrust in recommender systems[C]//European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases,At Skopje,2017:253-268.
[25]Hinton G E.A practical guide to training restricted Boltzmann machines.Neural networks:Tricks of the trade[J].Springer Berlin Heidelberg,2012,7700(8):599-619.
[26]Dempster A P.Upper and lower probabilities induced by a multivalued mapping[J].Annals of Mathematical Statistics,1967,38(2):325-339.
[27]Chen C,Zheng X,Zhu M,et al.Recommender system with composite social trust networks[J].International Journal of Web Services Research,2016,13(2):56-73.
[28]邹学强,包秀国,黄晓军,等.基于层次分析的微博短文本特征计算方法[J].通信学报,2016,37(12):50-55.Zou X Q,Bao X G,Huang X J,et al.Calculating the feature method of short text based on analytic hierarchy process[J],Journal on Communications,2016,37(12):50-55.
[29]Saaty T L,Peniwati K,Shang J S.The analytic hierarchy process and human resource allocation:Half the story[J].Mathematical&Computer Modelling,2007,46(7):1041-1053.
[30]Kim Y.Convolutional neural networks for sentence classification[C]//Proceeding of the 2014 Conference on Empirical methods in Natural Language Processing,2014:1746-1751.
[31]Kalchbrenner N,Grefenstette E,Blunsom P.A convolutional neural networks for modelling sentences[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics,USA,2014:655-665.
[32]Dhawan S,Simran.An enhanced mechanism of spam and category detection using Neuro-SVM[J].Procedia Computer Science,2018,132(5):429-436.
[33]Felipe V,Leonardo R,Elaine R,et al.Exploiting efficient and effective lazy Semi-Bayesian strategies for text classification[J].Neurocomputing,2018,307(11):153-171.
[34]Gao Q,Dong X,Fu D.A context-aware mobile user behavior based preference neighbor finding approach for personalized information retrieval[C]//Proceeding of Harms,2015:471-476.
[35]Golbeck J,Hendler J.FilmTrust:Movie recommendations using trust in Web-based social networks[C]//Proceeding of Consumer Communications&Networking Conference,2006:282-286.
[2]Li C Y,Ku Y C.The power of a thumbs-up:Will e-commerce switch to social commerce?[J].Information&Management,2018,55(3):340-357.
[3]Al-Oufi S,Kim H N,El Saddik A.A group trust metric for identifying people of trust in online social networks[J].Expert Systems with Applications,2012,39(18):13173-13181.
[4]Lingam G,Rout R R,Somayajulu D.Learning automata-based trust model for user recommendations in online social networks[J].Computers and Electrical Engineering,2018, 66(2):174-188.
[5]Li X T,Cong G,Li X L,et al.Rank-GeoFM:A ranking based geographical factorization method for point of interest recommendation[C]//Proceeding of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM Press,2015:433-442.
[6]Cheng F,Shen H,Zhao Y.Collaborative filtering recommendation algorithm for alleviating data sparsity[J].Journal of Modern Information,2016,36(4):33-38.
[7]Fletcher K K,Liu X F.A collaborative filtering method for personalized preference-based service recommendation[J].IEEE International Conference on Web Services,2015,2(2):400-407.
[8]王伟,王洪伟,孟园.协同过滤推荐算法研究:考虑在线评论情感倾向[J]·系统工程理论与实践,2014,34(12):3238-3249.Wang W,Wang H W,Meng Y.The collaborative filtering recommendation based on sentiment analysis of online reviews[J].Systems Engineering—Theory&Practice,2014,34(12):3238-3249.
[9]吕苗,金淳,韩庆平.基于情境化用户偏好的协同过滤推荐模型[J].系统工程理论与实践,2016,36(12):3244-3254.LüM,Jin C,Han J C.Improved collaborative filtering recommendation model based on contextualized user preference[J].Systems Engineering—Theory&Practice,2016,36(12):3244-3254.
[10]Georgiev K,Nakov P.A non-IID framework for collaborative filtering with restricted Boltzmann machines[C]//International Conference on Machine Learning,2013:1148-1156.
[11]Braunhofer M,Ricci F,Lamche B,et al.A context-aware model for proactive recommender systems in the tourism domain[C]//Proceeding of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct.New York:ACM,2015:1070-1075.
[12]Zhang X,Jia J,Gao K, et al.Trip outfits advisor:Location-oriented clothing recommendation[J].IEEE Transactions on Multimedia,2017,19(11):2533-2544.
[13]任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017(1):28-51.Ren X Y,Song M N,Song J D.Context-aware point-of-interest recommendation in location-based social networks[J].Chinese Journal of Computers,2017(1):28-51.
[14]胡春华,吴敏,刘国平.Web服务工作流中基于信任关系的QoS调度[J].计算机学报,2009,32(1):42-53.Hu C H,Wu M,Liu G H.QoS scheduling based on trust relationship in web service workflow[J].Chinese Journal of Computers,2009,32(1):42-53.
[15]胡勋,孟祥武,张玉洁,等.一种融合项目特征和移动用户信任关系的推荐算法[J].软件学报,2014,25(8):1817-1830.Hu X,Meng X W, Zhang Y J,et al.Recommendation algorithm combing item features and trust relationship of mobile users[J].Journal of Software,2014,25(8):1817-1830.
[16]Jamali M,Ester M.Trust Walker:A random walk model for combining trust-based and item-based recommendation[C]//Acm Sigkdd International Conference on Knowledge Discovery&Data Mining.ACM,2009:397-406.
[17]Ma H,Zhou D,Liu C,et al.Recommender systems with social regularization[C]//Proceedings of the Forth International Conference on Web Search and Web Data Mining,WSDM 2011,Hong Kong,China,2011:287-296.
[18]Ma H,Yang H X,Lyu M R,et al.SoRec:Social recommendation using probabilistic matrix factorization[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management, Napa Valley,California,USA,2008:931-940.
[19]Yang B,Lei Y,Liu D,et al.Social collaborative filtering by trust[C]//Proceeding of IJCAI'13 Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence,2013:2747-2753.
[20]Salakhutdinov R,Mnih A,Hinton G.Restricted Boltzmann machines for collaborative filtering[C]//International Conference on Machine Learning,ACM,Corvallis,Oregon,USA,2007:791-798.
[21]Deng S,Huang L,Xu G X,et al.On deep learning for trust-aware recommendations in social networks[J].IEEE Transactions on Neural Networks&Learning Systems,2017,28(9):1164-1177.
[22]Alonso J,Paredes R,Rosso P.Data mapping by restricted Boltzmann machines for social circles detection[C]//2015 International Joint Conference on Neural Networks,2015:1-7.
[23]Ma X,Lu H,Gan Z,et al.An explicit trust and distrust clustering based collaborative filtering recommendation approach[J].Electronic Commerce Research&Applications,2017,25(1):29-39.
[24]Rafailidis D,Crestani F.A regularization method with inference of trust and distrust in recommender systems[C]//European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases,At Skopje,2017:253-268.
[25]Hinton G E.A practical guide to training restricted Boltzmann machines.Neural networks:Tricks of the trade[J].Springer Berlin Heidelberg,2012,7700(8):599-619.
[26]Dempster A P.Upper and lower probabilities induced by a multivalued mapping[J].Annals of Mathematical Statistics,1967,38(2):325-339.
[27]Chen C,Zheng X,Zhu M,et al.Recommender system with composite social trust networks[J].International Journal of Web Services Research,2016,13(2):56-73.
[28]邹学强,包秀国,黄晓军,等.基于层次分析的微博短文本特征计算方法[J].通信学报,2016,37(12):50-55.Zou X Q,Bao X G,Huang X J,et al.Calculating the feature method of short text based on analytic hierarchy process[J],Journal on Communications,2016,37(12):50-55.
[29]Saaty T L,Peniwati K,Shang J S.The analytic hierarchy process and human resource allocation:Half the story[J].Mathematical&Computer Modelling,2007,46(7):1041-1053.
[30]Kim Y.Convolutional neural networks for sentence classification[C]//Proceeding of the 2014 Conference on Empirical methods in Natural Language Processing,2014:1746-1751.
[31]Kalchbrenner N,Grefenstette E,Blunsom P.A convolutional neural networks for modelling sentences[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics,USA,2014:655-665.
[32]Dhawan S,Simran.An enhanced mechanism of spam and category detection using Neuro-SVM[J].Procedia Computer Science,2018,132(5):429-436.
[33]Felipe V,Leonardo R,Elaine R,et al.Exploiting efficient and effective lazy Semi-Bayesian strategies for text classification[J].Neurocomputing,2018,307(11):153-171.
[34]Gao Q,Dong X,Fu D.A context-aware mobile user behavior based preference neighbor finding approach for personalized information retrieval[C]//Proceeding of Harms,2015:471-476.
[35]Golbeck J,Hendler J.FilmTrust:Movie recommendations using trust in Web-based social networks[C]//Proceeding of Consumer Communications&Networking Conference,2006:282-286.