Collaborative filtering algorithms are widely used in recommendationsystems. However, traditional collaborative filtering algorithms, which only use scoring information, have the defects of inaccurate similarity calculation and low personalization in actual scenarios and thus fail to meet user needs. For this reason, this study proposes an improved algorithm combined with user preferences and item attribute extension. Firstly, two improvements are made in the calculation of item similarity: Tag correlation is introduced to study the similarity between items; the extended attribute of items constructed according to the characteristics of the users who scored the item scan measure the item similarity in terms of item audience type. Secondly, considering the subjective preferences of users, a support vector machine is adopted to train the preference prediction model for each user, which can help to modify the item prediction score and improve the personalization and accuracy. Experimental results based on MovieLens dataset show that the proposed algorithm can calculate the similarity more accurately between items and get more accurate prediction scores according to users’ personalized preferences.