Abstract: Collaborative filtering algorithm is the most widely used algorithm in recommendation system.Its core is to use a group with similar interests to recommend information of interest for users.The traditional collaborative filtering algorithm uses the user item scoring matrix to calculate the similarity,and finds the similar groups of users through the similarity to recommend.However,due to the sparsity of the scoring matrix,the calculation of the similarity is not accurate enough,which indirectly leads to the degradation of the quality of the recommendation system.In order to alleviate the impact of data sparsity on the similarity calculation and improve the quality of recommendation,a similarity calculation method is proposed,which integrates user rating and user's explicit and implicit interest.This method first uses the user item scoring matrix to calculate the similarity of user's scoring,then uses the basic attribute of user and the user item scoring matrix to get the implicit attribute of project,then integrates the attribute of project category,the implicit attribute of project,the scoring matrix of user item and the scoring time of user to get the similarity of user's explicit and implicit interest,finally integrates the similarity of user's scoring and the similarity of user's explicit and implicit interest to find similar groups of users for recommendation.Experimental results on the data set Movielens show that compared with the traditional algorithm,which only uses a single scoring matrix to calculate the similarity,the new similarity calculation method can not only find similar groups of users more accurately,but also provide a better recommendation quality.

Key words: Collaborative filtering, Explicit and implicit interest, Item implicit attribute, User basic attributes, User rating