Publications

Mining User Interests from Social Media

Fattane Zarrinkalam and Stefano Faralli and Guangyuan Piao and Ebrahim Bagheri
Reference:
Fattane Zarrinkalam; Stefano Faralli; Guangyuan Piao and Ebrahim Bagheri Mining User Interests from Social Media. In The 29th ACM International Conference on Information and Knowledge Management (tutorial), (CIKM2020), 2020.
Links to Publication:
Abstract:
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: we will introduce (1) the information sources that are used for extracting user interests, (2) the variety of 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 will also introduce existing challenges, open research questions and exciting opportunities for further work.
Bibtex Entry:
@inproceedings{cikm2020-tutorial, author = {Fattane Zarrinkalam and Stefano Faralli and Guangyuan Piao and Ebrahim Bagheri}, abstract = {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: we will introduce (1) the information sources that are used for extracting user interests, (2) the variety of 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 will also introduce existing challenges, open research questions and exciting opportunities for further work.}, title = {Mining User Interests from Social Media}, booktitle = {The 29th ACM International Conference on Information and Knowledge Management (tutorial), (CIKM2020)}, year = {2020} }




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