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).

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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
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