Extracting, Mining and Predicting Users' Interests from Social Networks
Fattane Zarrinkalam, Hossein Fani, Ebrahim Bagheri · 2019
The abundance of user generated content on social networks provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) we introduce the information sources that are used for extracting user interests, (2) various types of user interest profiles that have been proposed in the literature, (3) techniques that have been adopted or proposed for mining user interests, (4) the scalability and resource requirements of the state of the art methods, and finally (5) the evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles. We also introduce existing challenges, open research question and exciting opportunities for further work.