News Recommenders: Real-Time, Real-Life Experiences

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Title: News Recommenders: Real-Time, Real-Life Experiences
Authors: Doychev, Doychin
Rafter, Rachael
Lawlor, Aonghus
Smyth, Barry
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Date: 3-Jun-2015
Online since: 2017-04-20T16:05:13Z
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
Start page: 337
End page: 342
Copyright (published version): 2015 Springer
Keywords: Recommender systemsNew recommender systemsReal-time recommendation frameworksAdapting/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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