A user-based aggregation topic model for understanding user's preference and intention in social network

In this study, we focus on understanding and mining user's preferences and intentions via user-based aggregation in the context of a social network. Understanding preference and intention in microblog texts is more difficult and challenging than understanding such characteristics in the context of standard text. The main reason is that search history and click history are difficult to obtain due to data privacy in social networks. Meanwhile, the text is sparse, and the number of background topics in social networks is enormous. To overcome the above challenges, we explore an indirect method of user's preference and intention understanding by leveraging a user-based aggregation topic model (UATM). Our UATM aims to mine the distributions of user's preferences and intentions by utilizing user's preference and intention distributions and followees' preference and intention distributions. Furthermore, to alleviate the sparsity problem, we discriminatively model common words and topic words and incorporate a user factor into our model. We combine the recurrent neural network (RNN) and inverse document frequency (IDF) as the weight prior to learn word relationships. Moreover, to further weaken the sparsity of context, we leverage word pairs to model topics for all documents. We also propose a collapsed Gibbs sampling algorithm to infer preference and intention in our UATM. To verify the effectiveness of the proposed method, we collect a Sina Weibo dataset consisting of microblog users and their pushed content to conduct various experiments. Both qualitative and quantitative evaluations demonstrate that our proposed UATM model outperforms several state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.