Abstract: Recommender systems are very useful due to the huge volume of information available on the Web．It helps users alleviate the information overload problem by recommending users with the personalized information,products or services．For content-based recommendation algorithm,there are few suitable similarity measures for the content-based recommendation methods to compute the similarity between items.This paper proposed a coupled object similarity based item recommendation algorithm．Our method firstly extracts item features from items,and then constructs item similarity model by using coupled object similarity measure．The collaborative filtering technique is then used to produce the recommendations for active users．Experimental results show that our proposed recommendation algorithm effectively solves the problem of similarity measure between items for recommendation algorithm and improves the quality of traditional content-based recommendation when lacking most of the item features．

Key words: Content-based recommendation system,Collaborative filtering,Coupled object similarity