Computer Science

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).

PDF (PC)

885

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.
[1] CHENG Zhang-tao, ZHONG Ting, ZHANG Sheng-ming, ZHOU Fan. Survey of Recommender Systems Based on Graph Learning [J]. Computer Science, 2022, 49(9): 1-13.
[2] WANG Guan-yu, ZHONG Ting, FENG Yu, ZHOU Fan. Collaborative Filtering Recommendation Method Based on Vector Quantization Coding [J]. Computer Science, 2022, 49(9): 48-54.
[3] SUN Xiao-han, ZHANG Li. Collaborative Filtering Recommendation Algorithm Based on Rating Region Subspace [J]. Computer Science, 2022, 49(7): 50-56.
[4] CAI Xiao-juan, TAN Wen-an. Improved Collaborative Filtering Algorithm Combining Similarity and Trust [J]. Computer Science, 2022, 49(6A): 238-241.
[5] HE Yi-chen, MAO Yi-jun, XIE Xian-fen, GU Wan-rong. Matrix Transformation and Factorization Based on Graph Partitioning by Vertex Separator for Recommendation [J]. Computer Science, 2022, 49(6A): 272-279.
[6] GUO Liang, YANG Xing-yao, YU Jiong, HAN Chen, HUANG Zhong-hao. Hybrid Recommender System Based on Attention Mechanisms and Gating Network [J]. Computer Science, 2022, 49(6): 158-164.
[7] DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35.
[8] ZHAN Wan-jiang, HONG Zhi-lin, FANG Lu-ping, WU Zhe-fu, LYU Yue-hua. Collaborative Filtering Recommendation Algorithm Based on Adversarial Learning [J]. Computer Science, 2021, 48(7): 172-177.
[9] SHAO Chao, SONG Shu-mi. Collaborative Filtering Recommendation Algorithm Based on User Preference Under Trust Relationship [J]. Computer Science, 2021, 48(6A): 240-245.
[10] WU Jian-xin, ZHANG Zhi-hong. Collaborative Filtering Recommendation Algorithm Based on User Rating and Similarity of Explicit and Implicit Interest [J]. Computer Science, 2021, 48(5): 147-154.
[11] XIAO Shi-tao, SHAO Ying-xia, SONG Wei-ping, CUI Bin. Hybrid Score Function for Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(3): 113-118.
[12] HAO Zhi-feng, LIAO Xiang-cai, WEN Wen, CAI Rui-chu. Collaborative Filtering Recommendation Algorithm Based on Multi-context Information [J]. Computer Science, 2021, 48(3): 168-173.
[13] LI Kang-lin, GU Tian-long, BIN Chen-zhong. Multi-space Interactive Collaborative Filtering Recommendation [J]. Computer Science, 2021, 48(12): 181-187.
[14] ZHU Yu-jie, LIU Hu-chen. Research on Multi-recommendation Fusion Algorithm of Online Shopping Platform [J]. Computer Science, 2021, 48(11A): 232-235.
[15] XU Bing, YI Pei-yu, WANG Jin-ce, PENG Jian. High-order Collaborative Filtering Recommendation System Based on Knowledge Graph Embedding [J]. Computer Science, 2021, 48(11A): 244-250.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.