News Recommenders: Real-Time, Real-Life Experiences
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|Title:||News Recommenders: Real-Time, Real-Life Experiences||Authors:||Doychev, Doychin
|Permanent link:||http://hdl.handle.net/10197/8442||Date:||3-Jun-2015||Abstract:||In this paper we share our experiences of working with a real-time news recommendation framework with real-world user and news data. We discuss the challenges faced while working in such a noisy but uniquely real-world context. Specifically, we focus on an initial evaluation of a 12 different news recommendation algorithms across 7 different German news sites, including general news, sports, business, and technology related news sites. We compare the performance of these algorithms, paying particular attention to their relative click-through rates and how this can vary with time of day and news domain.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Springer||Copyright (published version):||2015 Springer||Keywords:||Recommender systems;New recommender systems;Real-time recommendation frameworks;Adapting/contextualizing recommendations||DOI:||10.1007/978-3-319-20267-9_28||Language:||en||Status of Item:||Peer reviewed||Is part of:||Ricci, F., Bontcheva, K., Conlan, O. and Lawless, S. (eds.). Proceedings of UMAP 2015: 23rd International Conference on User Modeling, Adaptation, and Personalization||Conference Details:||UMAP 2015: 23rd International Conference on User Modeling, Adaptation, and Personalization, Dublin, Ireland, 29 June - 03 July 2017|
|Appears in Collections:||Insight Research Collection|
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