信任社交网络中改进的物质扩散推荐算法
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摘要
传统物质扩散推荐算法多样性低且未考虑用户所处的社交网络信息和物品的受欢迎程度。为此,在基于信任机制的社交网络中,提出一种改进的物质扩散推荐算法。引入信任机制形成目标用户的最优邻居集,模拟用户-物品二分网络,根据用户信任度对物品初始资源进行分配。考虑物品双向扩散能力并结合物品流行度的可调参数,实现资源再分配,从而优化目标用户的推荐结果。在真实数据集上的实验结果表明,该算法能在保证较高推荐准确率的同时,增强推荐结果的多样性。
Traditional mass diffusion recommendation algorithm has low diversity and its neglection to user's social network information and object popularity.Therefore,this paper proposes a mass diffusion recommendation algorithm based on trust mechanism in social network.The trust mechanism is introduced to form an optimal neighbor set for target user.By simulating on the user-object bipartite network,the initial object resources are reallocated according to trust mechanism.The object's bidirectional diffusion is taken into consideration,and some tunable parameters of object popularity are introduced to achieve resource redistribution,so that the better recommendation results for the target users can be gotten.Experimental results on real-world data sets reveal that the algorithm can enhance the diversity of recommendation results while ensuring higher recommendation accuracy.
Traditional mass diffusion recommendation algorithm has low diversity and its neglection to user's social network information and object popularity.Therefore,this paper proposes a mass diffusion recommendation algorithm based on trust mechanism in social network.The trust mechanism is introduced to form an optimal neighbor set for target user.By simulating on the user-object bipartite network,the initial object resources are reallocated according to trust mechanism.The object's bidirectional diffusion is taken into consideration,and some tunable parameters of object popularity are introduced to achieve resource redistribution,so that the better recommendation results for the target users can be gotten.Experimental results on real-world data sets reveal that the algorithm can enhance the diversity of recommendation results while ensuring higher recommendation accuracy.
引文
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[2] GOSWAMI S.Analysing effects of information overload on decision quality in an online environment[J].Journal of Management Research,2015,15(4):231-245.
[3] HE C,PARRA D,VERBERT K.Interactive recommender systems:a survey of the state of the art and future research challenges and opportunities[J].Expert Systems with Applications,2016,56:9-27.
[4] PESSEMIER T D,COPPENS S,GEEBELEN K,et al.Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform[J].Multimedia Tools and Applications,2012,58(1):167-213.
[5] 黄创光,印鉴,汪静,等.不确定近邻的协同过滤推荐算法[J].计算机学报,2010,33(8):1369-1377.
[6] SIPPEL S.Knowledge-based recommendations for cocktail recipes[EB/OL].[2017-10-14].http://www.10.1056/NEJM199809173391208.
[7] GARG N,WEBER I.Personalized,interactive tag recommendation for flickr[C]//Proceedings of ACM Conference on Recommender Systems.New York,USA:ACM Press,2008:67-74.
[8] BRAUNHOFER M,CODINA V,RICCI F.Switching hybrid for cold-starting context-aware recommender systems[EB/OL].[2017-10-14].http://www.10.1145/2645710.2645757.
[9] 周波,杨朝峰.基于二分网络的推荐算法研究进展[J].情报工程,2016,2(1):77-90.
[10] ZHOU T,Lü L,ZHANG Y C.Predicting missing links via local information[J].European Physical Journal B,2009,71(4):623-630.
[11] GUO Q,SONG W J,HOU L,et al.Effect of the time window on the heat-conduction information filtering model[J].Physica A:Statistical Mechanics and Its Applications,2014,401(5):15-21.
[12] LIU J G,HU Z,GUO Q.Effect of the social influence on topological properties of user-object bipartite networks[J].European Physical Journal B,2013,86(11):1-11.
[13] XU G,WU Z,ZHANG Y,et al.Social networking meets recommender systems[EB/OL].[2017-10-14].http://www.10.1504/IJSNM.2015.069773.
[14] CAI X,BAIN M,KRZYWICKI A,et al.Collaborative filtering for people to people recommendation in social networks[J].International Journal of Human-Computer Studies,2015,76(C):50-66.
[15] ZHOU T,REN J,MEDO M,et al.Bipartite network projection and personal recommendation[J].Physical Review E:Statistical,Nonlinear and Soft Matter Physics,2007,76(2).
[16] LIU J G,ZHOU T,GUO Q.Information filtering via biased heat conduction[J].Physical Review E:Statistical,Nonlinear and Soft Matter Physics,2011,84(3).
[17] MASSA P,AVESANI P.Trust-aware recommender systems[C]//Proceedings of ACM Conference on Recommender Systems.New York,USA:ACM Press,2007:17-24.
[18] YUAN W,GUAN D,LEE Y K,et al.Improved trust-aware recommender system using small-worldness of trust networks[J].Knowledge-Based Systems,2010,23(3):232-238.
[19] Lü L,LIU W.Information filtering via preferential diffusion[J].Physical Review E:Statistical,Nonlinear and Soft Matter Physics,2011,83(2).
[20] DAVIS J,GOADRICH M.The relationship between precision-recall and ROC curves[C]//Proceedings of International Conference on Machine Learning.New York,USA:ACM Press,2006:233-240.
[21] ZHU X,TIAN H,CAI S.Personalized recommendation with corrected similarity[EB/OL].[2017-10-14].https://arxiv.org/abs/1405.4095.
[22] YANG X,GUO Y,LIU Y,et al.A survey of collaborative filtering based social recommender systems[J].Computer Communications,2014,41(5):1-10.
[23] ZHOU T,JIANG L L,SU R Q,et al.Effect of initial configuration on network-based recommendation[J].Physics,2008,81(5).
[24] ZHU X,TIAN H,ZHANG P,et al.Personalized recommendation based on unbiased consistence[J].Europhysics Letters,2015,111(4).