新闻推荐的多维兴趣模型与传播分析

Analysis of News Diffusion in Recommender Systems Based on Multidimensional Tastes

如何将合适的信息推荐给合适的用户以满足用户的个性化需求,是推荐系统的基本问题。新兴的社会化推荐系统(social recommender system)通过兴趣相似的用户之间分享信息而达到个性化推荐的目的。使用多维兴趣向量刻画用户的兴趣,采用多智能体模型(multi-agent model)模拟,并引入用户和新闻的质量,分析了用户网络的结构特征以及质量因素对新闻推荐和传播的影响。实验结果表明:不同社区的主题不同,社区的中心用户兴趣专一,与社区的主题一致。此外,推荐中引入质量因素可以加快系统在高推荐成功率上的收敛速度,更能区分不同质量用户的粉丝数和不同质量新闻的传播深度与广度,增强了高质量用户和新闻的影响力,提高了系统中新闻推荐的专业水平。

How to deliver the right information to the fight person to meet the individual needs of users is a basic prob- lem 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 follo wer 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 catego- ry, 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 excel- lent users and news and improving the professional level of recommendation.