Zarrinkalam, Fattane and Piao, Guangyuan and Faralli, Stefano and Bagheri, Ebrahim (2020) Mining User Interests from Social Media. CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp. 3519-3520.

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Abstract

Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work.

Item Type:	Article
Keywords:	mining; user interests; social media;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15633
Identification Number:	https://doi.org/10.1145/3340531.3412167
Depositing User:	Guangyuan Piao
Date Deposited:	08 Mar 2022 12:16
Journal or Publication Title:	CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Publisher:	Association for Computing Machinery (ACM)
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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