A Probabilistic Framework for Temporal User Modeling on Microblogs

Abstract

In social media, users have contributed enormous behavior data online which can be leveraged for user modeling and conduct personalized services. Temporal user modeling, which incorporates the timestamp of these behavior data and understands users' interest evolution, have attracted attention recently. With the recognition that user interests are vulnerable to transient events, many current temporal user modeling solutions propose to first identify the transient events and then consider the identified events into user behavior modeling. In this work, in the context of microblogs, we propose a unified probabilistic framework to simultaneously model the process of transient event detection and temporal user tweeting. The outputs of the framework include: (1) one long-term topic space spanning over general categories, (2) one short-term topic space for each time interval corresponding to the transient events, and (3) users' interest distributions over the long- and short-term topic spaces. Qualitative and quantitative experimental evaluation are conducted on a large-scale Twitter dataset, with more than 2 million users and 0.3 billion tweets. The promising results demonstrate the advantage of the proposed topic models.