A Stream-Based Resource for Multi-Dimensional Evaluation of Recommender Algorithms

Kille, B., Lommatzsch, A., Hopfgartner, F. , Larson, M. and de Vries, A. P. (2017) A Stream-Based Resource for Multi-Dimensional Evaluation of Recommender Algorithms. In: The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017), Tokyo, Japan, 7-11 Aug 2017, pp. 1257-1260. ISBN 9781450350228 (doi:10.1145/3077136.3080726)

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Abstract

Recommender System research has evolved to focus on developing algorithms capable of high performance in online systems. This development calls for a new evaluation infrastructure that supports multi-dimensional evaluation of recommender systems. Today's researchers should analyze algorithms with respect to a variety of aspects including predictive performance and scalability. Researchers need to subject algorithms to realistic conditions in online A/B tests. We introduce two resources supporting such evaluation methodologies: the new data set of stream recommendation interactions released for CLEF NewsREEL 2017, and the new Open Recommendation Platform (ORP). The data set allows researchers to study a stream recommendation problem closely by "replaying" it locally, and ORP makes it possible to take this evaluation "live" in a living lab scenario. Specifically, ORP allows researchers to deploy their algorithms in a live stream to carry out A/B tests. To our knowledge, NewsREEL is the first online news recommender system resource to be put at the disposal of the research community. In order to encourage others to develop comparable resources for a wide range of domains, we present a list of practical lessons learned in the development of the dataset and ORP.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Hopfgartner, Dr Frank
Authors: Kille, B., Lommatzsch, A., Hopfgartner, F., Larson, M., and de Vries, A. P.
College/School:College of Arts > School of Humanities > Humanities Advanced Technology and Information Institute (HATII)
ISBN:9781450350228
Copyright Holders:Copyright © 2017 ACM
First Published:First published in The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017): 1257-1260
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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