Abstract:

In order to improve users’ experience, it is necessary to recommend valuable and interesting contents for users. We propose a label probability correlation based microblog recommendation method (LPCMR）via analyzing microblog features and the deficiencies of existing microblog recommendation algorithms. Firstly, our method takes advantage of the probability correlation between tags to construct the label similarity matrix. Then the weight of the tag for each user is enhanced based on the relevance weighting scheme, and the user tag matrix is constructed. The matrix is updated using the label similarity matrix, which contains both the user interest information and the relationship between tags. Experimental results show that the algorithm is effective in microblog recommendation.

Key words: probability correlation, microblog recommendation, user-label matrix, label weight