Cited By
View all- Cremonesi PSaid ATikk DZhou M(2016)Introduction to the Special Issue on Recommender System BenchmarkingACM Transactions on Intelligent Systems and Technology10.1145/28706277:3(1-4)Online publication date: 8-Mar-2016
Report on the SIGIR 2013 workshop on benchmarking adaptive retrieval and recommender systems
Abstract
In recent years, immense progress has been made in the development of recommendation, retrieval, and personalisation techniques. The evaluation of these systems is still based on traditional information retrieval and statistics metrics, e.g., precision, recall and/or RMSE, often not taking the use-case and situation of the actual system into consideration. However, the rapid evolution of recommender and adaptive IR systems in both their goals and their bapplication domains foster the need for new evaluation methodologies and environments. In the Workshop on Benchmarking Adaptive Retrieval and Recommender Systems, we aimed to provide a platform for discussions on novel evaluation and benchmarking approaches.
References
[1]
Nicholas J. Belkin. Some(what) grand challenges for information retrieval. SIGIR Forum, 42(1):47--54, 2008.
[2]
Benjamin Kille, Frank Hopfgartner, Torben Brodt, and Tobias Heintz. The plista dataset. In NRS'13: Proceedings of the International Workshop and Challenge on News Recommender Systems, pages 14--21. ACM, 10 2013.
[3]
Alan Said, Jimmy Lin, Alejandro Bellogín, and Arjen de Vries. A month in the life of a production news recommender system. In Proceedings of the CIKM Workshop on Living Labs for Information Retrieval Evaluation, LivingLab. ACM, 11 2013. to appear.
[4]
Mozhgan Tavakolifard, Jon Atle Gulla, Kevin C. Almeroth, Frank Hopfgartner, Benjamin Kille, Till Plumbaum, Andreas Lommatzsch, Torben Brodt, Arthur Bucko, and Tobias Heintz. Workshop and challenge on news recommender systems. In RecSys'13: Proceedings of the International ACM Conference on Recommender Systems. ACM, 10 2013.
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- Report on the SIGIR 2013 workshop on benchmarking adaptive retrieval and recommender systems
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Published In
December 2013
135 pages
Copyright © 2013 Authors.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 January 2013
Published in SIGIR Volume 47, Issue 2
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Cited By
View all- Cremonesi PSaid ATikk DZhou M(2016)Introduction to the Special Issue on Recommender System BenchmarkingACM Transactions on Intelligent Systems and Technology10.1145/28706277:3(1-4)Online publication date: 8-Mar-2016
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