Exploring Tweet Engagement in the RecSys 2014 Data Challenge
|Title:||Exploring Tweet Engagement in the RecSys 2014 Data Challenge||Authors:||Wasilewski, Jacek
Hurley, Neil J.
|Permanent link:||http://hdl.handle.net/10197/6109||Date:||Oct-2014||Abstract:||While much recommender system research has been driven by the rating prediction task, there is an emphasis in recent research on exploring new methods to evaluate the effectiveness of a recommendation. The Recommender Systems 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 differs 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 profile to distinguish topical movies from other movies. Our pre-diction engine also exploits the observation that extreme ratings are more likely to attract engagement.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2014 ACM||Keywords:||Machine Learning & Statistics; Information systems applications; Twitter||DOI:||10.1145/2668067.2668075||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the 2014 Recommended Systems Challenge||Conference Details:||8th ACM Conference on Recommender Systems, Foster City, Silicon Valley, USA, 6-10 October 2014|
|Appears in Collections:||Insight Research Collection|
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