USER BEHAVIOR PREDICTION BY FDNN MODEL ON SOCIAL NETWORKS

Main Article Content

LE CHANG
LIDONG WANG
KANG AN

Abstract

Following the development of social networks, understanding user behavior can help some tasks in user recommendation, information dissemination, e-commerce recommendation and etc. Due to the non-availability of user data, it is still remain challenge to predict user behavior with high accuracy. In this paper, we propose a deep neural network FDNN (Fusion Deep Neural Network) based on BLSTM to predict two kinds of user behaviors simultaneously, such as comment and retweeting. Our model utilizes BLSTM to obtain high quality embedding space for user history and the new query post.  We design a concatenation layer, a hidden layer and an output layer for behavior prediction. The experimental results on Sina dataset show that the proposed method could achieve better results than other traditional methods.

Keywords:
User behavior prediction, BLSTM, deep neural network, social network

Article Details

How to Cite
CHANG, L., WANG, L., & AN, K. (2021). USER BEHAVIOR PREDICTION BY FDNN MODEL ON SOCIAL NETWORKS. Asian Journal of Mathematics and Computer Research, 28(4), 20-25. Retrieved from https://www.ikprress.org/index.php/AJOMCOR/article/view/7144
Section
Original Research Article

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