Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 142-148.doi: 10.11896/jsjkx.210400173

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

Personalized News Recommendation Algorithm with Enhanced List Information and User Interests

PU Qian-qian, LEI Hang, LI Zhen-hao, LI Xiao-yu   

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
  • Received:2021-04-17 Revised:2021-08-04 Online:2022-06-15 Published:2022-06-08
  • About author:PU Qian-qian,born in 1998,postgra-duate.Her main research interests include machine learning and recommendation systems.
    LEI Hang,born in 1960,Ph.D,professor,Ph.D supervisor.His main research interests include embedded system and big data analysis.
  • Supported by:
    Sichuan Science and Technology Program(2018GFW0198).

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Abstract: With the continuous expansion of data and information,the point-to-point recommendation model,as a commonly used recommendation algorithm in deep learning,can deal with the problem of overloaded information to some extent.However,it predicts the recommendation score only by a single user and an isolated news,without using of the interactive information among rele-vant lists of news.To improve the quality of personalized recommendation,it is urgent for current news recommendation platforms to figure out how to accurately and comprehensively represent users and news by taking full advantage of users’ browsing history,semantic meaning of news as well as list information.In view of this,this paper puts forward a personalized news recommendation algorithm with improved list information and user interest.Based on the historically browsed news sequence of the user and news data,the point-to-point recommendation model is trained for representation construction to realize the tailored information extraction catering to the users’ interest,and the list information is enhanced by processing the characteristics of the user and news lists through the attention network,thus realizing the direct recommendation ranking of the lists as a whole.Experimental results show that this personalized recommendation algorithm with enhanced list information and user attraction can model global the comprehensive list information,presenting a significantly improved effect compared with cutting-edge news re-commendation algorithms at present.

Key words: Attention network, Listwise algorithm, News recommendation, Recommendation algorithm, User personalization

CLC Number: 

  • TP183
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