Exploring Tweet Engagement in the RecSys 2014 Data Challenge

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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 & StatisticsInformation systems applicationsTwitter
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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