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Exploring Tweet Engagement in the RecSys 2014 Data Challenge

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
While much recommender system research has been driven by the rating prediction task, there is an emphasis in re- cent research on exploring new methods to evaluate the ef- fectiveness of a recommendation. The Recommender Sys- tems Challenge 2014 takes up this theme by challenging re- searchers to explore engagement as an evaluation criterion. In this paper we discuss how predicting engagement di ers from the traditional rating prediction task and motivate the rationale behind our approach to the challenge. We show that standard matrix factorization recommender algorithms do not perform well on the task. Our solution depends on clustering items according to their time-dependent pro le to distinguish topical movies from other movies. Our pre- diction engine also exploits the observation that extreme ratings are more likely to attract engagement.
Conference Name: 
8th ACM Conference on Recommender Systems
Proceedings: 
8th ACM Conference on Recommender Systems
Digital Object Identifer (DOI): 
10.na
Publication Date: 
06/10/2014
Conference Location: 
United States of America
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
Yes
Publication document: