Accurate Recommendation Approach of Psychological Consultation Information Based on User Portrait and TAG_SVD_CF

Abstract

A hybrid recommendation algorithm of psychological counseling information based on user profile and item tag attribute with singular value decomposition(SVD) technology is developed. To solve the problem of data sparsity of the recommendation algorithm, the SVD technology is applied to collaborative filtering algorithm for optimizing the user item rating matrix. The recommendation algorithm includes two parts: generating medical user profile in accurate recommendation of medical information, and realizing the storage, query and update of user profile, where the index system of medical portrait was established from demographic attribute, interest label dimension and business social dimension. The performances of the developed algorithm are investigated by compared with the traditional cosine similarity and Pearson similarity. The results show that the proposed similarity has a lower mean absolute error and significantly improves the accuracy of the system.