关键词: 个性化推荐, 网络爬虫, 网站聚合, 本体论, 用户偏好, 冷启动

Abstract: To solve the shortcomings in traditional methods of personalized recommendation such as sparsity,coldstart,overspecialization and low accuracy problem,this paper proposes a recommendation method based on Website aggregation and knowledge.It gets a movie set to be recommended by Web crawler aggregating Websites,and also builds an ontologybased film model based on which that proposes an algorithm for learning the weights of user preference.It measures the similarity between movies using SimRank method and the weighted average to recommend to users according to the level of similarity.Experimental results show that the accuracy of this method is improved by about ten percent than the existing methods when it is used on nonrealtime recommendation.And quality of recommendations is improved significantly on realtime recommendation.In some extent,sparsity,coldstart,overspecialization problem can be solved.

Key words: personalized recommendation, Web crawler, Website aggregation, ontology, user preference, coldstart