TLRank:一种新的社会化协同排序推荐算法
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摘要
已有的社会化协同排序推荐算法的研究只是简单地融入用户的社交网络信息,没有考虑用户之间社会化信任网络的传递性;同时,该推荐算法的性能面临数据高度稀疏性问题的挑战.为了进一步解决这些问题,在传统的协同排序推荐算法(ListRank,List-wise Learning to Rank)和最新的社会化协同过滤算法(Trust MF,Social Collaborative Filtering by Trust)的基础上,提出了一种新的社会化协同排序推荐算法(TLRank),融合均高度稀疏的用户的显式评分数据和社会化信任网络数据,以进一步增强协同排序推荐算法的性能.实验结果表明:在各个评价指标下,TLRank算法的性能均优于几个经典的协同排序推荐算法,且复杂度低、运算时间与评分点个数线性相关; TLRank算法的推荐精度高、可扩展性好,适合处理大数据,可广泛运用于互联网信息推荐领域.
The previous research on social collaborative ranking(SCR) recommendation algorithm only integrates the user's social network information into their model,but does not take into account the transmission of social trust network between users. A problem of the previous research on SCR is data sparsity and cold start that severely degrades quality of recommendation. To solve the problem,a new social collaborative ranking recommendation algorithm(TLRank) based on ListRank(List-wise Learning to Rank) algorithm and the newest TrustMF(Social Collaborative Filtering by Trust) algorithm is proposed to improve the performance of collaborative ranking recommendation by means of elaborately integrating twofold sparse information,the conventional rating data given by users and the social network among the same users. Experimental results on practical datasets show that our proposed TLRank algorithm outperforms the existing CR approaches with reference to the different evaluation metrics and that the algorithm has the advantage of low complexity and is shown to be linear with the number of observed ratings in a given user-item rating matrix. Because of its high precision and good expansibility,TLRank is suitable for processing big data and has a prospect of wide application in the field of internet information recommendation.
The previous research on social collaborative ranking(SCR) recommendation algorithm only integrates the user's social network information into their model,but does not take into account the transmission of social trust network between users. A problem of the previous research on SCR is data sparsity and cold start that severely degrades quality of recommendation. To solve the problem,a new social collaborative ranking recommendation algorithm(TLRank) based on ListRank(List-wise Learning to Rank) algorithm and the newest TrustMF(Social Collaborative Filtering by Trust) algorithm is proposed to improve the performance of collaborative ranking recommendation by means of elaborately integrating twofold sparse information,the conventional rating data given by users and the social network among the same users. Experimental results on practical datasets show that our proposed TLRank algorithm outperforms the existing CR approaches with reference to the different evaluation metrics and that the algorithm has the advantage of low complexity and is shown to be linear with the number of observed ratings in a given user-item rating matrix. Because of its high precision and good expansibility,TLRank is suitable for processing big data and has a prospect of wide application in the field of internet information recommendation.
引文
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[2] SHI Y,LARSON M,HANJALIC A. Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation[J]. Information Sciences,2013,229:29-39.
[3] LI G,ZHANG Z Q,WANG L Y,et al. One-class collaborative filtering based on rating prediction and ranking prediction[J]. Knowledge-Based Systems,2017,124:46-54.
[4] LI G,CHEN Y F,ZHANG Z Q,et al. Social personalized ranking with both the explicit and implicit influence of user trust and of item ratings[J]. Engineering Applications of Artificial Intelligence,2018,67(1):283-295.
[5] LI G,CHEN Q. Exploiting explicit and implicit feedbacks for personalized ranking[J]. Mathematical Problems in Engineering,2016:2535329/1-12.
[6] ZHAO L L,LU Z Q,PAN J L,et al. Matrix factorization+for movie recommendation[C]∥Proceedings of the 25th International Joint Conference on Artificial Intelligence.New York:ACM,2016:307-315.
[7] SALAKHUTDINOV R,MNIH A. Probabilistic matrix factorization[J]. Advances in Neural Information Processing Systems,2007,17(5):1257-1264.
[8] WEIMER M,KARATZOGLOU A,LE Q V,et al. CofiRankmaximum margin matrix factorization for collaborative ranking[C]∥Proceedings of the 21th International Conference on Neural Information Processing Systems. Vancouver:Curran Associates Inc,2007:79-86.
[9] LIU N N,YANG Q. EigenRank:a ranking-oriented approach to collaborative filtering[C]∥Proceedings of the31th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM,2008:83-90.
[10] SHI Y,KARATZOGLOU A,BALTRUNAS L,et al. x CLiMF:optimizing expected reciprocal rank for data with multiple levels of relevance[C]∥Proceedings of the Sixth ACM Conference on Recommender Systems. New York:ACM,2013:431-433.
[11] SHI Y,KARATZOGLOU A,BALTRUNAS L,et al. CLi MF:collaborative less-is-more filtering[C]∥Proceedings of the 23rd International Joint Conference on Artificial Intelligence. New York:ACM,2013:3077-3081.
[12] SHI Y,LARSON M,HANJALIC A. List-wise learning to rank with matrix factorization for collaborative filtering[C]∥Proceedings of the Fourth ACM Conference on Recommender Systems. New York:ACM,2010:269-272.
[13] YANG B,LEI Y,LIU D,et al. Social collaborative filtering by trust[C]∥Proceedings of the 23rd International Joint Conference on Artificial Intelligence. New York:ACM,2013:2747-2753.
[14] LIU N N,ZHAO M,YANG Q. Probabilistic latent preference analysis for collaborative filtering[C]∥Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York:ACM,2009:759-766.
[15] MA H,YANG H X,LYU M R,et al. SoRec:social recommendation using probabilistic matrix factorization[C]∥Proceedings of the 21rd ACM International Conference on Information and Knowledge Management. New York:ACM,2008:146-155.
[16] MA H,KING I,LYU M R. Learning to recommendation with explicit and implicit social relations[C]∥Proceedings of the 21rd ACM International Conference on Information Retrieval. New York:ACM,2009:2901-2919.
[17] MA H,ZHOU D Y,LIU C,et al. Recommendation systems with social regularization[C]∥Proceedings of the4th ACM International Conference on Web Search and Data Mining. New York:ACM,2011:287-296.
[18] JAMALI M,ESTER M. A matrix factorization technique with trust propagation for recommendation in social networks[C]∥Proceedings of the Twenty-third International Conference on Artificial Intelligence. New York:ACM,2011:2644-2649.
[19] GUO G B,ZHANG J,KORKE-SMITH N. A novel recommendation model regularized with user trust and item ratings[J]. IEEE Transactions on Knowledge and Data Engineering,2016,28(7):1607-1620.
[20] KOREN Y. Factor in the neighbors:scalable and accurate collaborative filtering[J]. ACM Transactions on Knowledge Discovery from Data,2010,4(1):1-24.
[21] LIU Y,YANG J J. Improving ranking-based recommendation by social information and negative similarity[J].Procedia Computer Science,2015,55:732-740.
[22] YAO W L,HE J,HUANG G Y,et al. SoRank:incorporating social information into learning to rank models for recommendation[C]∥Proceedings of the 23rd International Conference on World Wide Web. New York:ACM,2014:409-410.