Collaborative Filtering Algorithm Based on User Characteristic and Time Weight

This paper proposes a collaborative filtering recommendation algorithm based on user characteristics and time weight which focuses on the data sparseness and cold start problems of collaborative filtering algorithms. First, digitize user's characteristics in the dataset and calculate the similarity degree of the user's feature, then weight the similarity calculation formula with the integration time function to obtain the comprehensive similarity so that a more accurate prediction score is obtained. The comparison experiments showed that the algorithm can reduce the sparseness of the data set effectively when the data is extremely sparse, and to some extent, it alleviates the cold start problem and improves the prediction accuracy of the recommendation algorithm.

[1]  Bradley N. Miller,et al.  MovieLens unplugged: experiences with an occasionally connected recommender system , 2003, IUI '03.

[2]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[3]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[4]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[5]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[6]  Liu Fa-shen.  A collaborative filtering recommendation algorithm based on user characteristic attribute and cloud model , 2014 .

[7]  Hong Hou,et al.  The exploration on global village language: Harmony , 2017, ICSCA.

[8]  Jonathan L. Herlocker,et al.  A collaborative filtering algorithm and evaluation metric that accurately model the user experience , 2004, SIGIR '04.

[9]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[10]  David M. Pennock,et al.  Categories and Subject Descriptors , 2001 .

[11]  Hui Tian,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014, Knowl. Based Syst..

[12]  John Riedl,et al.  Ganging up on Information Overload , 1998, Computer.

[13]  Hao Chen,et al.  An improved collaborative recommendation algorithm based on optimized user similarity , 2016, The Journal of Supercomputing.

[14]  LiuHaifeng,et al.  A new user similarity model to improve the accuracy of collaborative filtering , 2014 .

[15]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[16]  Rajendra Akerkar,et al.  Knowledge Based Systems , 2017, Encyclopedia of GIS.

[17]  Jason J. Jung,et al.  Item-Based Collaborative Filtering with Attribute Correlation: A Case Study on Movie Recommendation , 2014, ACIIDS.

[18]  Yuzhao Liu,et al.  A Collaborative Filtering Algorithm Based on Time Period Partition , 2010, 2010 Third International Symposium on Intelligent Information Technology and Security Informatics.