Abstract: In order to solve the problem of low accuracy in user similarity calculation of traditional collaborative filtering algorithm, an item attribute and item tag information is introduced into the recommendation system, and proposes a hybrid recommendation algorithm is proposed based on tags and attributes. Firstly, the user's score on the item is transformed into the user's score on the item attribute value and label, and the user-attribute rating matrix and user-tag rating matrix are constructed as user description files. Then the similarity between users is calculated according to the user-attribute rating matrix and user-tag rating matrix, and the results are average weighted to obtain the nearest neighbor list of each user. Finally, the recommendation result is generated according to the neighbor's score on the item. Since the number of item attributes and major tags are much lower than the number of items, the algorithm can effectively solve the sparsity problem of collaborative filtering algorithm, and describe the user preference more intuitively. In the process of constructing the user description file, considering the law that the user preference changes with time, different weights are given to the user's scores at different time points, and the weight increases gradually with the passage of time. Experimental results show that the proposed algorithm can predict users' ratings of unrated items more accurately and improve the accuracy and recall of recommendations.

Key words: personalized recommendation； , collaborative filtering； , tags； , attributes； , time weight