Computer Science ›› 2017, Vol. 44 ›› Issue (7): 227-231.doi: 10.11896/j.issn.1002-137X.2017.07.040

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Time-based Local Collaborative Filtering Recommendation Algorithm on Tensor Factorization

SUN Yan-ge, WANG Zhi-hai and HUANG Dan   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Traditional recommendation models are stationary with neglecting time factor.Some recommendation algorithms take time factor into consideration,but what they do is using the latest data or reducing the weight of past data.It may lead to the loss of some useful information.To solve the above problem,a time-based local low-rank tensor factorization algorithm was proposed.In contract to standard collaborative filtering algorithms,our method does not assume that the rating matrix is low-rank.We relaxed the assumption and assumed that the rating matrix is locally low-rank.The algorithm takes time factor into consideration and views rating matrix as 3-dimensional sensor based on the traditional recommendation algorithms which extend the traditional algorithms to tensor field.Experiments show that the algorithm could improve the efficiency of ranking recommendation.

Key words: Recommendation system,Time factor,Tensor factorization,Local low-rank

[1] GANTZ J,REINSEL D.IDC:The digital universe in 2020:Big data,bigger digital shadows,and biggest growth in the far east .http://www.emc.com/leadship/digital-universe/2012view/index.htm.
[2] SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques[J].Advances in Artificial Intelligence,2009,9(4):1-19.
[3] DING Y,LI X.Time weight collaborative filtering[C]∥Proceedings of the 14th ACM International Conference on Information and Knowledge Management.New York,USA:ACM,2005:485-492.
[4] GONG S J,CHENG G H.Mining user interest change for improving collaborative filtering[C]∥Proceedings of the 2008 Se-cond International Symposium on Intelligent Information Technology Application.Washington,USA:IEEE Computer Society,2008:24-27.
[5] LEE T Q,PARK Y,PARK Y T.A time-based approach to effective recommender systems using implicit feedback[J].Expert Systems with Applications,2008,4(4):3055-3062.
[6] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,7(6):734-749.
[7] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥Procee-dings of the 10th International Conference on World Wide Web.New York,USA:ACM,2001:285-295.
[8] BREESE J S,HECKERMAN D,KADIE C.Empirical analysisof predictive algorithms for collaborative filtering[C]∥Procee-dings of the 14th Conference on Uncertainty in Artificial Intelligence.San Francisco,USA:Morgan Kaufmann Publishers,1998:43-52.
[9] PAVLOV D,PENNOCK D.A maximum entropy approach to collaborative filtering in dynamic,sparse,high-dimensional domains[C]∥Proceedings of the 16th Annual Conference on Neural Information Processing Systems.MIT Press,2002:1441-1448.
[10] ZHANG J W,YANG Z.Collaborative filtering recommendation algorithm based on improved user clustering[J].Computer Science,2014,41(12):176-178.(in Chinese) 张峻玮,杨洲.一种基于改进的层次聚类的协同过滤用户推荐算法研究[J].计算机科学,2014,41(12):176-178.
[11] YIN H,CUI B,SUN Y,et al.LCARS:A Spatial Item Recommender System[J].ACM Transactions on Information Systems (TOIS),2014,2(3):1-37.
[12] SALAKHUTDINOV R,MNIH A.Bayesian probabilistic matrix factorization using Markov chain Monte Carlo[C]∥ International Conference on Machine Learning.ACM,2008:880-887.
[13] LEE J,KIM S,LEBANON G,et al.Local low-rank matrix approximation[J].Journal of Machine Learning Research,2013,8(2):82-90.
[14] LEE J,BENGIO S,KIM S,et al.Local collaborative ranking[C]∥Proceedings of the 23rd International Conference on World Wide Web (WWW 2014).Springer,2014:85-96.
[15] LIU T Y.Learning to rank for information retrieval[J].Foundations and Trends in Information Retrieval,2009,3(3):225-331.
[16] LIU H Y,WANG Z H,HUANG D,et al.Listwise Collaborative Ranking Based on the Assumption of Locally Low-Rank Rating Matrix[J].Journal of Software,2015,6(11):2981-2993.(in Chinese) 刘海洋,王志海,黄丹,等.基于评分矩阵局部低秩假设的成列协同排名算法[J].软件学报,2015,26(11):2981-2993.
[17] KOLDA T G,BADER B W.Tensor decompositions and applications [J].SIAM Review,2009,1(3):455-500.
[18] SALAKHUTDINOV R,MNIH A.Bayesian probabilistic matrix factorization using markov chain monte carlo[C]∥Proceedings of the 25th International Conference on Machine Learning.New York,USA:ACM,2008:880-887.

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