A Stream-based Resource for Multi-Dimensional Evaluation of Recommender Algorithms
| Autor: | Kille, B., Lommatzsch, A., Hopfgartner, F., Larson, M., Vries, A.P.d., Kando, N. |
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| Přispěvatelé: | Kando, N. |
| Rok vydání: | 2017 |
| Předmět: |
recommender system
multi--dimensional benchmarking Computer science Data Science Subject (documents) 02 engineering and technology Recommender system streams Language & Communication Variety (cybernetics) World Wide Web Resource (project management) 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data set (IBM mainframe) Algorithm |
| Zdroj: | Kando, N. (ed.), SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan — August 07-11, 2017, 1257-1260. New York : ACM STARTPAGE=1257;ENDPAGE=1260;TITLE=Kando, N. (ed.), SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan — August 07-11, 2017 SIGIR Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 Kando, N. (ed.), SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan — August 07-11, 2017, pp. 1257-1260 |
| Popis: | 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 livinglab 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 thedevelopment of the dataset and ORP. |
| Databáze: | OpenAIRE |
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