Abstract: Aiming at the problem of imperfect user information acquisition and long recommendation time in the current mainstream recommendation algorithms, this paper proposes a recommendation algorithm based on user information vector clustering and improved SAMME. The algorithm analyzes basic user information (region, time, interest, tags, etc) to find user information keywords; weights different user information keywords based on the TF-IDF method to construct user information vectors; then uses the K-means algorithm to perform user clustering analysis, and uses the user clustering results as improved SAMME training sample set; finally, the prediction results are recommended to the user by the improved SAMME algorithm, and the model is saved during the training process, which greatly reduces the recommendation time. Finally, the algorithm of this paper is tested on the real Weibo user data set and compared with other mainstream algorithms. The results show that the algorithm of this paper  achieves good results in accuracy, recall and F-value.

Key words: recommendation system, SAMME algorithm, user information, cluster analysis