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Reproducibility and Replication in Recommender Systems EvaluationWorkshop in conjunction with ACM RecSys 2013. Hong Kong, October 12.
http://repsys.project.cwi.nl/
Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12.
http://repsys.project.cwi.nl/
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Reproducibility and Replication in Recommender Systems Evaluation | repsys.project.cwi.nl Reviews
https://repsys.project.cwi.nl
Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12.
repsys.project.cwi.nl
Invited keynote « Reproducibility and Replication in Recommender Systems Evaluation
http://repsys.project.cwi.nl/2013/07/invited-keynote
Reproducibility and Replication in Recommender Systems Evaluation. Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12. Laquo; Proceedings in ACM ICPS. Workshop has been accepted. We are pleased to announce that Mark Levy will be giving the opening keynote at the RepSys workshop. Is Senior Data Scientist at Mendeley. 26 July, 2013. Notification: 16 August, 2013. 6 September, 2013. Workshop: 12 October, 2013. Report on our #recsys2013. Workshop at ACM #SIGIR. 02:06:16 PM July 14, 2014.
Related Events « Reproducibility and Replication in Recommender Systems Evaluation
http://repsys.project.cwi.nl/related-events
Reproducibility and Replication in Recommender Systems Evaluation. Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12. Open Source ML @ NIPS 2006. Recommendation Systems in Software Engineering. ACM TIST Special Issue on Benchmarking Recommender Systems. 26 July, 2013. Notification: 16 August, 2013. 6 September, 2013. Workshop: 12 October, 2013. Report on our #recsys2013. Workshop at ACM #SIGIR. Forum http:/ t.co/5sDujlhbgx. 02:06:16 PM July 14, 2014. From Twitter Web Client.
Workshop has been accepted « Reproducibility and Replication in Recommender Systems Evaluation
http://repsys.project.cwi.nl/2013/05/accepted
Reproducibility and Replication in Recommender Systems Evaluation. Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12. Laquo; Invited keynote. We are happy to announce that the workshop has been accepted to be held in conjunction with ACM RecSys 2013. We hope to see you all in Hong Kong! 26 July, 2013. Notification: 16 August, 2013. 6 September, 2013. Workshop: 12 October, 2013. Report on our #recsys2013. Workshop at ACM #SIGIR. Forum http:/ t.co/5sDujlhbgx. 02:06:16 PM July 14, 2014.
Submission « Reproducibility and Replication in Recommender Systems Evaluation
http://repsys.project.cwi.nl/submission
Reproducibility and Replication in Recommender Systems Evaluation. Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12. There are two categories of paper submissions: Long paper. Submissions should report on substantial contributions of lasting value. Each accepted long paper will be presented in a plenary session of the workshop program. The maximum length is 8 pages. Short paper. All submissions should be prepared according to the ACM SIG Proceedings Templates. Please see Call for Papers.
Program « Reproducibility and Replication in Recommender Systems Evaluation
http://repsys.project.cwi.nl/program
Reproducibility and Replication in Recommender Systems Evaluation. Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12. 8220;Offline evaluation of recommender systems: all pain and no gain? 8221; [ slides. 8211; Mark Levy. How to improve the statistical power of the 10-fold cross validation scheme in Recommender Systems. Andrej Košir, Ante Odić and Marko Tkalčič. Research Paper Recommender System Evaluation: A Quantitative Literature Survey. Joseph Konstan and Gediminas Adomavicius.
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data-science-in-action.blogspot.com
Data Science in Action: July 2013
http://data-science-in-action.blogspot.com/2013_07_01_archive.html
Data Science in Action. Thursday, 11 July 2013. Are your recommendations any good? If you've worked much with recommender systems you'll know that offline evaluation of recommendation algorithms can bring on a serious headache in no time. It was nice of Netflix to offer researchers $1M. Here are some of the pain points that I've bumped into recently:. Public datasets aren't enough for (easily) reproducible research: take a look at the results reported here. Toolkits need to scale: MyMediaLite. For exampl...
Events | Information Retrieval Group
http://ir.ii.uam.es/events
Recommender Systems Evaluation: Dimensions and Design (REDD 2014). Co-organized by the IR Group at the 8. ACM Conference on Recommender Systems ( RecSys 2014. The workshop was held in October 2014 in Foster City, Silicon Valey, CA, USA. International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys 2013). Co-organized by the IR Group at the 7. ACM Conference on Recommender Systems ( RecSys 2013. The workshop was held in October 2013 in Hong Kong, China. Was held on 26...
data-science-in-action.blogspot.com
Data Science in Action: Are your recommendations any good?
http://data-science-in-action.blogspot.com/2013/07/are-your-recommendations-any-good.html
Data Science in Action. Thursday, 11 July 2013. Are your recommendations any good? If you've worked much with recommender systems you'll know that offline evaluation of recommendation algorithms can bring on a serious headache in no time. It was nice of Netflix to offer researchers $1M. Here are some of the pain points that I've bumped into recently:. Public datasets aren't enough for (easily) reproducible research: take a look at the results reported here. Toolkits need to scale: MyMediaLite. For exampl...
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Reproducibility and Replication in Recommender Systems Evaluation
Reproducibility and Replication in Recommender Systems Evaluation. Workshop in conjunction with ACM RecSys 2013. Hong Kong, October 12. Experiment replication and reproduction are central to the concept of empirical evaluation of Recommender Systems (RS), which is still an open issue in the area of recommendation. When an experiment is repeated by a different researcher and exactly the same result is obtained, we can say the experiment has been replicated. Proceedings in ACM ICPS. Proceedings in ACM ICPS.
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