Computer Science ›› 2017, Vol. 44 ›› Issue (8): 230-235.doi: 10.11896/j.issn.1002-137X.2017.08.039

Previous Articles     Next Articles

Collaborative Filtering Recommendation Algorithm Combing Category Information and User Interests

HE Ming, XIAO Run, LIU Wei-shi and SUN Wang   

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

Abstract: Collaborative filtering is the most successful and widely used information technology to make personalized prediction by exploiting the historical behaviors of users.The accuracy of the recommendation depends on effectiveness of the similarity measure.The methods of traditional similarity measure,which mainly concern with the similarity of the common ratings but ignore the category information in the rated items,are suffering from data sparsity problem.To address this issue,we proposed a ratings matrix filling method which is based on classification information by combining with user interest similarity calculation method and consider the category information fully to make the measure of interest more realistic.The experimental results show that the proposed algorithm can relieve the influence of the sparsity of user-item ratings on collaborative filtering algorithm and improve recommendation accuracy,diversity,and novelty.

Key words: Collaborative filtering,Recommendation systems,Interest,Similarity computation

[1] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-art and possible extensions[J].Transactions on Knowledge and Data Engineering,2005,7(6):734-749.
[2] XU H L,WU X,LI X D,et al.Comparison study of Internet re-commendation system [J].Journal of Software,2009,0(2):350-362.(in Chinese) 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362.
[3] AHN H J.A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem[J].Information Sciences,2008,178(1):37-51.
[4] DAI J L.Study on the sparsity problem of collaborative filtering algorithm [D].Chongqing:Chongqing University,2013.(in Chinese) 代金龙.协同过滤算法中数据稀疏性问题研究[D].重庆:重庆大学,2013.
[5] DENG A L,ZHU Y Y,SHI B L.A collaborative filtering re-commendation algorithm based on item ratingprediction[J].Journal of Software,2003,4(9):1621-1628.(in Chinese) 邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9):1621-1628.
[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,2005,17(6):734-749.
[7] JI X S,LIU Y B,LUO L M.Similarity measurement based on interest in collaborative filtering [J].Journal of Computer Applications,2010,0(10):2618-2620.(in Chinese) 嵇晓声,刘宴兵,罗来明.协同过滤中基于用户兴趣度的相似性度量方法[J].计算机应用,2010,30(10):2618-2620.
[8] CLEGER-TAMAYO S,FERNNDEZ-LUNA J M,HUETE J F.Top-N news recommendations in digital newspapers[J].Knowledge-Based Systems,2012,27(6):180-189.
[9] ZHANG X S.Research on collaborative filtering recommenda-tion algorithms for data sparsity[D].Hefei:University of Science & Technology China,2011.(in Chinese) 张学胜.面向数据稀疏的协同过滤推荐算法研究[D].合肥:中国科学技术大学,2011.
[10] YU X.Research on recommendation methodsbased on collaborative filtering techniques[D].Tianjin:Tianjin University,2009.(in Chinese) 郁雪.基于协同过滤技术的推荐方法研究[D].天津:天津大学,2009.
[11] FAN B,CHENG J J.Collaborative filtering recommendation algorithm based on user’s multi-similarity[J].Computer Science,2012,9(1):23-26.(in Chinese) 范波,程久军.用户间多相似度协同过滤推荐算法[J].计算机科学,2012,39(1):23-26.
[12] MILLER B N,ALBERT I,LAM S K,et al.MovieLens unplug-ged:experiences with an occasionally connected recommender system[C]∥Proceedings of the 8th International Conference on Intelligent User Interfaces.ACM,2003:263-266.
[13] LUO X,OUYANG Y X,XIONG Z,et al.The effect of similarity support in K-Nearest-Neighborhood based collaborative filtering [J].Chinese Journal of Computers,2010,3(8):1437-1445.(in Chinese) 罗辛,欧阳元新,熊璋,等.通过相似度支持度优化基于K近邻的协同过滤算法[J].计算机学报,2010,33(8):1437-1445.
[14] STECK H.Evaluation of recommendations:rating-predictionand ranking[C]∥Proceedings of the 7th ACM Conference on Recommender Systems.ACM,2013:213-220.
[15] SHI Y,LARSON M,HANJALIC A.Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation[J].Information Sciences,2013,229(6):29-39.
[16] Apache.Mahout[EB/OL].[2016-06-03].http://mahout.apache.org.
[17] MovieLens datasets [EB/OL].[2016-06-16].http://group-lens.org/datasets/movielens.
[18] KOREN Y,BELL R M,VOLINSKY C.Matrix factorization te-chniques for recommender systems[J].IEEE Computer,2009,42(8):30-37.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!