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Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies

Publication Type: 
Edited Conference Meeting Proceeding
Abstract: 
User modeling based on the user-generated content of users on social networks such as Twitter has been studied widely, and has been used to provide personalized recommendations via inferred user interest pro les. Most previous studies have focused on active users who actively post tweets, and the corresponding inferred user interest profi les are generated by analyzing these users' tweets. However, there are also a great number of passive users who only consume information from Twitter but do not post any tweets. In this paper, we propose a user modeling approach using the biographies (i.e, self descriptions in Twitter profi les) of a user's followees (i.e., the accounts that they follow) to infer user interest pro les for passive users. We evaluate our user modeling strategy in the context of a link recommender system on Twitter. Results show that exploring the biographies of a user's followees improves the quality of user modeling signi cantly compared to two state-of-the-art approaches leveraging the names and tweets of followees.
Conference Name: 
39th European Conference on Information Retrieval
Proceedings: 
39th European Conference on Information Retrieval
Digital Object Identifer (DOI): 
10.XXX
Publication Date: 
08/04/2017
Conference Location: 
United Kingdom (excluding Northern Ireland)
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
No