User embedding for scholarly microblog recommendation

Abstract

Nowadays, many scholarly messages are posted on Chinese microblogs and more and more researchers tend to find scholarly information on microblogs. In order to exploit microblogging to benefit scientific research, we propose a scholarly microblog recommendation system in this study. It automatically collects and mines scholarly information from Chinese microblogs, and makes personalized recommendations to researchers. We propose two different neural network models which learn the vector representations for both users and microblog texts. Then the recommendation is accomplished based on the similarity between a user's vector and a microblog text's vector. We also build a dataset for this task. The two embedding models are evaluated on the dataset and show good results compared to several baselines.