The abundance of user generated content on social media 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 will cover five important aspects related to the effective mining of user interests:
The information sources that are used for extracting user interests
Various types of user interest profiles that have been proposed in the literature
Techniques that have been adopted or proposed for mining user interests
The scalability and resource requirements of the state of the art methods
The evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles. We will also introduce existing challenges, open research question and exciting opportunities for further work.
The target audience for this tutorial will be those who have familiarity with social media mining and basics of data mining techniques. Where appropriate the tutorial will not make any assumptions about the audience’s knowledge on more advanced techniques such as link prediction, matrix factorization, deep matching, entity linking and knowledge-graph based reasoning, among others. As such, sufficient details will be provided as appropriate so that the content will be accessible and understandable to those who have fundamental understanding of data mining principals. The tutorial will only assume familiarity with topics included in an undergraduate data mining course.