Computer Science ›› 2018, Vol. 45 ›› Issue (4): 247-251.doi: 10.11896/j.issn.1002-137X.2018.04.041

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Collaborative Filtering Recommendation Algorithm Based on Tag Clustering and Item Topic

LI Hao-yang and FU Yun-qing   

  • Online:2018-04-15 Published:2018-05-11

Abstract: The traditional item-based collaborative filtering algorithm only focuses on the rating data without the chara-cteristics of items when calculating the similarity between items.The appearance of social tagging can reflect the characteristics of items,but there are some semantic fuzziness problems while adding the social tags into the collaborative filtering algorithm directly.To solve the problems above,this paper put forward an improved item-based collaborative filtering recommendation algorithm.It clusters social tags to generate tag clusters which represent different topics,and calculates the relevance between items and topics to generate item-topics matrix according to the tagging results of items.The similarity between items is calculated by combining item-topics matrix with item-ratings matrix,the rating of target items are predicted through the collaborative filtering algorithm,and the personalized recommendation is realized.Expe-rimental results on MovieLens dataset show that the proposed algorithm can eliminate the semantic fuzziness and improve the quality of recommendation.

Key words: Social tagging,Tag clustering,Item topics,Collaborative filtering,Personalized recommendation

[1] BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recommender systems survey[J].Knowledge-based Systems,2013,46(1):109-132.
[2] RESNICK P,IACOVOU N,SUCHAK M,et al.GroupLens:an open architecture for collaborative filtering of netnews[C]∥ACM Conference on Computer Supported Cooperative Work.ACM,1994:175-186.
[3] LINDEN G,SMITH B,YORK recommenda-tions:item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80.
[4] FENG W,WANG J.Incorporating heterogeneous informationfor personalized tag recommendation in social tagging systems[C]∥ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2012:1276-1284.
[5] ZHANG B,GAO Y,GAO K N,et al.Combining Relation and Content Analysis for Social Tagging Recommendation[J].Journal of Software,2012,23(3):476-488.(in Chinese) 张斌,高引,高克宁,等.融合关系与内容分析的社会标签推荐[J].软件学报,2012,23(3):476-488.
[6] SONG H,LU P,ZHAO K.Improving item-based collaborative filtering recommendation system with tag[C]∥International Conference on Artificial Intelligence,Management Science and Electronic Commerce.IEEE,2011:2142-2145.
[7] CAI Q,HAN D M,LI H S,et al.Personal Resource Recommendation Based on Tags and Collaborative Filtering[J].Computer Science,2014,41(1):69-71.(in Chinese) 蔡强,韩东梅,李海生,等.基于标签和协同过滤的个性化资源推荐[J].计算机科学,2014,41(1):69-71.
[8] LI G,WANG S,LI Z Y,et al.Personalized Tag Recommendation Algorithm Based on Tensor Decomposition[J].Computer Science,2015,41(4):30-35.(in Chinese) 李贵,王爽,李征宇,等.基于张量分解的个性化标签推荐算法[J].计算机科学,2015,41(4):30-35.
[9] WANG W P,WANG J H.Hybrid Recommendation MethodBased on Tag and Collaborative Filtering[J].Computer Engineering,2011,37(14):34-35.(in Chinese) 王卫平,王金辉.基于Tag和协同过滤的混合推荐方法[J].计算机工程,2011,37(14):34-35.
[10] KIM H N,JI A T,HA I,et al.Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation[J].Electronic Commerce Research & Applications,2010,9(1):73-83.
[11] HALPIN H,ROBU V,SHEPHERD H.The complex dynamics of collaborative tagging[C]∥International Conference on World Wide Web(WWW 2007).Banff,Alberta,Canada,May.DBLP,2007:211-220.
[12] WU C,ZHOU B.Complex network analysis of tag as a social network[J].Journal of Zhejiang University(Engineering Scie-nce),2010,4(11):2194-2197.(in Chinese) 吴超,周波.基于复杂网络的社会化标签分析[J].浙江大学学报:工学版,2010,4(11):2194-2197.
[13] AHN Y,BAGROW J,LEHMANN S.Link Communites Reveal Multiscale Complexity Innetworks[J].Nature,2010,466(7307):761-764.
[14] GroupLens[EB/OL].
[15] SARWAR B,KARYPIS G,KONSTAN J,et al.Item-based collaborative filtering recommendation algorithms[C]∥International Conference on World Wide Web.ACM,2001:285-295.
[16] JI H,LI J,REN C,et al.Hybrid collaborative filtering model for improved recommendation[C]∥IEEE International Conference on Service Operations and Logistics,and Informatics.IEEE,2013:142-145.

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