Computer Science ›› 2021, Vol. 48 ›› Issue (2): 114-120.doi: 10.11896/jsjkx.200900152

• Database & Big Data & Data Science • Previous Articles     Next Articles

User Cold Start Recommendation Model Integrating User Attributes and Item Popularity

HAN Li-feng, CHEN Li   

  1. School of Information Science & Technology,Northwest University,Xi'an 710127,China
  • Received:2020-09-21 Revised:2020-10-18 Online:2021-02-15 Published:2021-02-04
  • About author:HAN Li-feng,born in 1980,Ph.D,is a member of China Computer Federation.His main research interests include recommendation system,knowledge graph,business intelligence,etc.
    CHEN Li,born in 1963,professor,Ph.D supervisor,is a member of China Computer Federation.Her main research interests include intelligent information processing,data mining,network secu-rity,etc.
  • Supported by:
    The Key R&D Program of Shaanxi Province(2019ZDLGY10-01).

Abstract: Cold start has always been a closely watched issue in the field of recommendation systems.Aiming at the problem of cold start for newly registered users,this paper proposes a recommendation model that integrates user demographic information and item popularity.The training set users are divided into several categories by clustering the training set users,and then the distance between the new user and other users in the category is calculated,and the neighboring user set is selected.When calcula-ting the score,we consider comprehensively the impact of popularity,and then push the programs of interest to target users.Finally,the proposed model is verified on the classic recommendation system data set.The results show that the model is significantly better than the traditional collaborative filtering algorithm and has a certain mitigation effect on the cold start problem.

Key words: Collaborative filtering, Item popularity, Recommended system, Social statistics information, User cold star

CLC Number: 

  • TP391
[1] EDMUNDS A,MORRIS A.The problem of information overload in business organisations:a review of the literature[J].International Journal of Information Management,2000,20(1):17-28.
[2] RESNICK P,VARIAN H R.Recommender systems[J].Communications of the ACM,1997,40(3):56-58.
[3] VERBERT K,MANOUSELIS N,OCHOA X,et al.Context-aware recommender systems for learning:a survey and future challenges[J].IEEE Transactions on Learning Technologies,2012,5(4):318-335.
[4] 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.
[5] WANG J,ZHAO H Y,CHEN Q K,et al.Representative Item Selection for Cold Start Users[J].Journal of Chinese Computer Systems,2019,40(8):1589-1594.
[6] DUAN D K,FU X F.Research on user cold start problem in hybrid collaborative filtering algorithm[J].Computer Engineering and Applications,2017,53(21):151-156.
[7] CHEN K H,HAN P P,WU J.User Clustering Based Social Network Recommendation[J].Hinese Journal of Computers,2013,36(2):349-359.
[8] YANG X M,SUN Y,WANG M J,et al.Research on the Problem of User Cold-start in News Recommendation Systems[J].Journal of Chinese Computer Systems,2016,37(3):479-482.
[9] SEDHAIN S,SANNER S,BRAZIUNAS D D,et al.Social Collaborative Filtering for Cold-start Recommendations[C]//Proceedings of the 8th ACM Conference on Recommender systems.New York:ACM Press,2014:345-348.
[10] ZHAO Z L,WANG C D,WAN Y Y,et al.FTMF:Recommendation in social network with Feature Transfer and Probabilistic Matrix Factorization[C]// International Joint Conference on Neural Networks (IJCNN).Piscataway:IEEE Press,2016:847-854.
[11] TANG L,JIANG Y X,LI L,et al.Ensemble contextual bandits for personalized recommendation[C]//Proceedings of the 8th ACM Conference on Recommender Systems.New York:ACM Press,2014:73-80.
[12] LEI Z,NOROOZI V,YU P S.Joint Deep Modeling of Users and Items Using Reviews for Recommendation[C]// Tenth Acm International Conference on Web Search & Data Mining.New York:ACM Press,2017:425-434.
[13] CHEN C,MENG X,XU Z,et al.Location-aware Personalized News Recommendation with Deep Semantic Analysis[J].IEEE Access,2017,5:1624-1638.
[14] GONG Y,ZHANG Q.Hashtag Recommendation Using Attention-Based Convolutional Neural Network[C]// Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.Palo Alto:AAAI Press,2016:2782-2788.
[15] LEI C,DONG L,LI W,et al.Comparative Deep Learning of Hybrid Representations for Image Recommendations [C]// Computer Vision & Pattern Recognition.Piscataway:IEEE Press,2016:2545-2553.
[16] AARON V,DIELEMAN S,SCHRAUWEN B.Deep content-based music recommendation[J].Advances in Neural Information Processing Systems,2013,26:2643-2651.
[17] WANG Y,QU J,LIU J,et al.What to Tag Your Microblog:Hashtag Recommendation Based on Topic Analysis and Colla-borative Filtering[C]//Asia-Pacific Web Conference.Switzerland:Springer Press,2014:610-618.
[18] HAN J W,KAMBER M,PEI J.Data Mining:Concepts andTechniques:Concepts and Techniques[J].Data Mining Concepts Models Methods & Algorithms Second Edition,2011,5(4):1-18.
[19] WAGSTAFF K L,CARDIE C,ROGERS S,et al.Constrained K-means Clustering with Background Knowledge[C]// international conference on machine learning.San Francisco:Margan Kaufmann Press,2001:577-584.
[20] WANG W,YANG J,MUNTZ R.STING:A statistical information grid approach to spatial data mining[C]// Proceedings of 23rd International Conference on Very Large Data Bases.1997:186-195.
[21] CHENG Z P,ZHOU D,WANG C.CLINCH:Clustering Incomplete High-Dimensional Data for Data Mining Application[C]// Web Technologies Research and Development-APWeb 2005.Berlin:Springer Press,2005:88-99.
[22] HE L,HU P.Cold start recommendation model based on user multi-dimension trust[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2018,30(6):827-834.
[23] QUINLAN J R.Induction of Decision Trees[J].Machine Learning,1986,1(1):81-106.
[24] BOBADILLA J,ORTEGA F,HERNANDO A,et al.Recom-mender systems survey[J].Knowledge-Based Systems,2013,46:109-132.
[25] WANG Y,WAN X Y,TAO Y Z,et al.Collaborative filtering recommendation algorithm based on K-medoids item clustering[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2017,29(4):521-526.
[26] YE F,ZHANG H.A collaborative filtering recommendationbased on users' interest and correlation of items[C]// 2016 International Conference on Audio,Language and Image.Piscataway:IEEE Press,2016:515-520.
[27] LIKA B,KOLOMVATSOS K,HADJIEFTHYMIADES S.Facing the cold start problem in recommender systems[J].Expert Systems with Applications,2014,41(4):2065-2073.
[28] SON L H,MINH N T H,CUONG K M,et al.An application of fuzzy geographically clustering for solving the cold-start problem in recommender systems[C]// Proceeding of 5th IEEE International Conference of Soft Computing and Pattern Recognition (SoCPaR2013).Piscataway:IEEE Press,2013:44-49.
[29] LIU H,HU Z,MIAN A,et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge-Based Systems,2014,56:156-166.
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