基于多维信任和联合矩阵分解的社会化推荐方法
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摘要
针对现有社会化推荐算法在信任分析方面的不足,研究了从社交辅助信息中充分挖掘用户信任关系的方法,进而提出一种基于多维信任计算和联合矩阵分解的社会化推荐算法。首先,从用户社交行为、社交圈特征获得用户的动态和静态两种局部信任度,再利用信任网络的结构特征提取全局信任度;然后,构造一种对增强关注矩阵和社交信任矩阵进行联合矩阵分解的社会化推荐算法,并采用随机梯度下降法对其求解。基于新浪微博数据集的实验结果表明,所提出的算法在推荐精度和Top-K推荐能力方面明显优于socailMF、 LOCABAL、 contextMF和TBSVD这几种代表性的社会化推荐算法。
Aiming at the shortages in trust analysis of existing social recommendation algorithms, a social recommendation algorithm based on multi-dimensional trust and collective matrix factorization was proposed with full use of user trust relationship mined from social auxiliary information. Firstly, the dynamic and static local trust relationships were extracted respectively from social interaction behaviors and social circle features of the user, and the global trust relationship was extracted from the structural features of trust network. Then, a social recommendation algorithm was presented by collective factorizing the enhanced following relationship matrix and the social trust relationship matrix, and a stochastic gradient descent method was utilized to solve the algorithm. The experimental results on the Sina microblog dataset indicate that the proposed algorithm outperforms some popular social recommendation algorithms such as socialMF, LOCABAL, contextMF and TBSVD(Trust Based Singular Value Decomposition), in terms of recommendation accuracy and Top-K performance.
Aiming at the shortages in trust analysis of existing social recommendation algorithms, a social recommendation algorithm based on multi-dimensional trust and collective matrix factorization was proposed with full use of user trust relationship mined from social auxiliary information. Firstly, the dynamic and static local trust relationships were extracted respectively from social interaction behaviors and social circle features of the user, and the global trust relationship was extracted from the structural features of trust network. Then, a social recommendation algorithm was presented by collective factorizing the enhanced following relationship matrix and the social trust relationship matrix, and a stochastic gradient descent method was utilized to solve the algorithm. The experimental results on the Sina microblog dataset indicate that the proposed algorithm outperforms some popular social recommendation algorithms such as socialMF, LOCABAL, contextMF and TBSVD(Trust Based Singular Value Decomposition), in terms of recommendation accuracy and Top-K performance.
引文
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[3]MA H,KING I,LYU M R.Learning to recommend with social trust ensemble[C]//Proceedings of the 32nd International ACM SIGIRConference on Research and Development in Information Retrieval.New York:ACM,2009:203-210.
[4]JAMALI M,ESTER M.A matrix factorization technique with trust propagation for recommendation in social networks[C]//Proceedings of the 4th ACM Conference on Recommender Systems.New York:ACM,2010:135-142.
[5]郭磊,马军,陈竹敏.一种信任关系强度敏感的社会化推荐算法[J].计算机研究与发展,2013,50(9):1805-1813.(GUOL,MA J,CHEN Z M.Trust strength aware social recommendation method[J].Journal of Computer Research and Development,2013,50(9):1805-1813.)
[6]WANG X,WANG Y,GUO J H.Building trust networks in the absence of trust relations[J].Frontiers of Information Technology&Electronic Engineering,2017,18(10):1591-1600.
[7]TANG J,HU X,GAO H,et al.Exploiting local and global social context for recommendation[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence.Menlo Park,CA:AAAI Press,2013:2712-2718.
[8]余永红,高阳,王皓,等.融合用户社会地位和矩阵分解的推荐算法[J].计算机研究与发展,2018,55(1):113-124.(YUY H,GAO Y,WANG H,et al.Integrating user social status and matrix factorization for item recommendation[J].Journal of Computer Research and Development,2018,55(1):113-124.)
[9]WANG M,MA J.A novel recommendation approach based on users'weighted trust relations and the rating similarities[J].Soft Computing,2015,20(10):1-10.
[10]潘一腾,何发智,于海平.一种基于信任关系隐含相似度的社会化推荐算法[J].计算机学报,2018,41(1):65-81.(PANY T,HE F Z,YU H P.Social Recommendation algorithm using implicit similarity in trust[J].Chinese Journal of Computers,2018,41(1):65-81.)
[11]MA H,JIA M,ZHANG D,et al.Combining tag correlation and user social relation for microblog recommendation[J].Information Sciences,2017,385(C):325-337.
[12]JIANG M,CUI P,WANG F.Scalable recommendation with social contextual information[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(11):2789-2802.
[13]XU X,YUAN D.A novel matrix factorization recommendation algorithm fusing social trust and behaviors in micro-blogs[C]//Proceedings of the 2017 IEEE 2nd IEEE International Conference on Cloud Computing and Big Data Analysis.Piscataway,NJ:IEEE,2017:283-287.
[14]ZHANG Y,CHEN W,YIN Z.Collaborative filtering with social regularization for TV program recommendation[J].KnowledgeBased Systems,2013,54(4):310-317.
[15]BAO H,LI Q,LIAO S S,et al.A new temporal and social PMF-based method to predict users'interests in micro-blogging[J].Decision Support Systems,2013,55(3):698-709.
[16]ZHANG J,LIU B,TANG J.Social influence locality for modeling retweeting behaviors[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence.San Francisco:AAAIPress,2013:2761-2767.
[17]陈婷,朱青,周梦溪,等.社交网络环境下基于信任的推荐算法[J].软件学报,2017,28(3):721-731.(CHEN T,ZHUQ,ZHOU M X,et al.Trust-based recommendation algorithm in social network[J].Journal of Software,2017,28(3):721-731.)
[18]SINGH A P,GORDON G J.Relational learning via collective matrix factorization[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2008:650-658.