Computer Science ›› 2013, Vol. 40 ›› Issue (11): 126-130.

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Analysis of News Diffusion in Recommender Systems Based on Multidimensional Tastes

WANG Guan-nan,CHEN Duan-bing and FU Yan   

  • Online:2018-11-16 Published:2018-11-16

Abstract: How to deliver the right information to the right person to meet the individual needs of users is a basic problem in recommender systems.The emerging social recommender systems are personalized ones with sharing information of similar interest users.Using multidimensional vectors to characterize the user’s interest,simulating with multi-agent model,and factoring the quality of the users and news into recommendation,this paper analyzed impact of leader-follower network the structure and the quality factors on the recommendations and diffusion of news.The results indicate that different communities have different themes,and the core users of communities not only concentrate on one category,but also share the same interest with the community theme.Additionally,introducing the quality of users and news into systems not only can speed up the convergence of higher success rate of recommendation,but also can distinguish the followers of different users and the behaviors of propagation of different news,while raising the influence of excellent users and news and improving the professional level of recommendation.

Key words: Social recommender systems,Multidimensional tastes,Similarity of users,Structure of communities,News diffusion

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