The multi-tag semantic correlation used for micro-blog user interest modeling

Abstract Tags play an important role in expressing the interests and attributes of microblog users, but the implicit semantic meaning of user tags is often ignored. We propose an improved micro-blog user interest modeling approach based on multi-tag semantic correlation via analyzing tag relationship and the limitations of the existing micro-blog user interest models. Firstly, the co-occurrence frequency of tag pair is calculated from the micro-blog user collection to obtain the intra-correlation between tag pair, the path is constructed based on the linking tags for each tag pair and the inter-correlation of tag pair is obtained via the shared entropy. Secondly, usertags are clustered and representative tags are obtained according to the similarity between tags, to construct the tags-representative tags matrix. Finally, we combine the above two correlations to acquire the semantic correlation matrix, based on which the users-tags matrix can be updated, thus the micro-blog user interest model based on multi-tag semantic correlation can be obtained. We evaluate our method through a series of experiments based on a dataset crawled from the open API and the results are analyzed. The results show that the interest expression model of microblog users proposed in this paper can better represent the interest characteristics of users.

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