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News Recommenders: Real-Time, Real-Life Experiences
Date Issued
2015-06-03
Date Available
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Start Page
337
End Page
342
Copyright (Published Version)
2015 Springer
Language
English
Status of Item
Peer reviewed
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
This item is made available under a Creative Commons License
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