Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 224-229.

• Data Science • Previous Articles     Next Articles

Top-N Personalized Recommendation Algorithm Based on Tag

MA Wen-kai, LI Gui, LI Zheng-yu, HAN Zi-yang, CAO Ke-yan   

  1. (Faculty of Information & Control Engineering,Shenyang Jianzhu University,Shenyang 110168,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: With the development of Web2.0,UGC tag system is receiving more and more attention.Tag can not only reflect users’ interests,but also it can describe the innate character of item.Available tag recommendation algorithm does not considerae the influence of continuous behaviors of users.Although traditional recommendation algorithm based on Markov Chain produces recommendation through the emphasis on the research of continuous behaviors of users,it can not be appliedy to the tag recommendation of UCG due to its direct function on the two-dimensional relationships between user and item.Therefore,according to the thoughts of Markov Chain and Collaborative Filtering,an individual recommendation algorithm based on the tag could be applied.The algorithm splits three-dimensional relationships of 〈user-tag-item〉 into two two-dimensional relationships of 〈user-tag〉 and 〈tag-user〉.Firstly,the interest degree is calculated through the application of Markov Chain.Then correspondent item matched through the recommendation of tags.To raise the accuracy rating of recommendation,modeling of satisfaction is established by this tag according to the influence of tags and associated relationships among tags of items .This model is a kind of probabilistic model.At the same time of calculating the interest degree and satisfaction degree of user-tag and user-item,the thought of Collaborative Filtering is also used to complement sparse data.Compared with available algorithm,this algorithm is improved a lot in the aspects of precision and recall rate on the open data set.

Key words: Collaborative filtering, Markov chain, Recommended system, Satisfaction model, Tag

CLC Number: 

  • TP301.6
[1]CHEN W,HSU W,LEE M L.A unified framework for recommendations based on quaternary semantic analysis[C]∥International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2011:1023-1032.
[3]RENDLE S,FREUDENTHALER C,SCHMIDTTHIEME L.Factorizing personalized Markov chains for next-basket recommendation[C]∥International Conference on World Wide Web.ACM,2010.
[5]SHANI G,HECKERMAN D,BRAFMAN R I.An MDP-Based Recommender System[J].Journal of Machine Learning Research,2005,6(1):1265-1295.
[6]BRIN S,PAGE L.The anatomy of a large-scale hypertextualWeb search engine[C]∥International Conference on World Wide Web.Elsevier Science Publishers B.V.,1998:107-117.
[7]HOTHO A,JÄSCHKE R,SCHMITZ C,et al.Information Retrieval in Folksonomies:Search and Ranking[J].Semantic Web Research & Applications,2006,4011:411-426.
[8]PHAM T A N,LI X,CONG G.A General Model for Out-of-town Region Recommendation[C]∥International Conference.2017.
[9]PIRASTEH P,JUNG J J,HWANG D.Item-Based Collaborative Filtering with Attribute Correlation:A Case Study on Movie Recommendation[M]∥Intelligent Information and Database Systems.Springer International Publishing,2014.
[12]YE M,YIN P F,LEE W C,et al.Exploiting Geographical In-fluence for Collaborative Point-of-interest Recommendation[C]∥International AcmSigir Conference on Research & Development in Information Retrieval.ACM,2011.
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