Publications

Extracting, Mining and Predicting Users' 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 Extracting, Mining and Predicting Users' Interests from Social Media. In Foundations and Trends in Information Retrieval (FnTIR), 2020.
Links to Publication: [www][pdf]
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 paper, we will cover five important subjects related to the mining of user interests from social media: (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.
Bibtex Entry:
@article{fntir20, title={Extracting, Mining and Predicting Users' Interests from Social Media}, journal={Foundations and Trends in Information Retrieval (FnTIR)}, 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 paper, we will cover five important subjects related to the mining of user interests from social media: (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.}, year = {2020}, webpdf={http://ls3.rnet.ryerson.ca/wiki/images/3/33/User_Interest_Modeling_V3.pdf}, url={https://www.nowpublishers.com/INR} }




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