Hybrid microblog recommendation with heterogeneous features using deep neural network

With the development of mobile Internet, microblog has become one of the most popular social platforms. The enormous user-generated microblogs have caused the problem of information overload, which makes users difficult to find the microblogs they actually need. Hence, how to provide users with accurate microblogs has become a hot and urgent issue. In this paper, we propose an approach of hybrid microblog recommendation, which is developed on a framework of deep neural network with a group of heterogeneous features as its input. Specifically, two new recommendation strategies are first constructed in terms of the extended user-interest tags and user interest topics, respectively. These two strategies additionally with the collaborative filtering are employed together to obtain the candidate microblogs for final recommendation. Then, we propose the heterogeneous features related to personal interests of users, interest in authors and microblog quality to describe the candidate microblogs. Finally, a deep neural network with multiple hidden layers is designed to predict and rank the microblogs. Extensive experiments conducted on the datasets of Sina Weibo and Twitter indicate that our proposed approach significantly outperforms the state-of-the-art methods. The code and the two datasets of this paper are publicly available at GitHub.

Automated summary

This paper proposes a hybrid microblog recommendation approach based on a deep neural network, incorporating user interest tags and topics, describing candidate microblogs with features and utilizing collaborative filtering to obtain recommended microblogs. Experimental results demonstrate that the proposed method significantly outperforms existing methods.