Assessing ranking metrics in top-N recommendation-Reference-Cited by-同舟云学术

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Assessing ranking metrics in top-N recommendation

Published:2020-06-08 Issue:4 Volume:23 Page:411-448
ISSN:1386-4564
Container-title:Information Retrieval Journal
language:en
Short-container-title:Inf Retrieval J

Author:

Valcarce Daniel,Bellogín Alejandro,Parapar Javier^ORCID,Castells Pablo

Funder

Ministerio de Ciencia, Innovación y Universidades

Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10791-020-09377-x.pdf

Reference74 articles.

1. Allan, J., Croft, B., Moffat, A., & Sanderson, M. (2012). Frontiers, challenges, and opportunities for information retrieval: Report from SWIRL 2012 the second strategic workshop on information retrieval in lorne. SIGIR Forum, 46(1), 2–32. https://doi.org/10.1145/2215676.2215678.

2. Anderson, C. (2008). The Long Tail: Why the Future of Business Is Selling Less of More. New York: Hachette Books.

3. Beel, J., & Langer, S. (2015). A comparison of offline evaluations, online evaluations, and user studies in the context of research-paper recommender systems. In S. Kapidakis, C. Mazurek, M. Werla (eds.) Proceedings of the 19th international conference on theory and practice of digital libraries, TPDL ’15 (pp. 153–168). Springer, Cham. https://doi.org/10.1007/978-3-319-24592-8_12.

4. Belkin, N. J., & Croft, W. B. (1992). Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM, 35(12), 29–38. https://doi.org/10.1145/138859.138861.

5. Bellogín, A., Castells, P., & Cantador, I. (2011). Precision-oriented evaluation of recommender systems. In Proceedings of the 5th ACM conference on recommender systems, RecSys ’11 (p. 333). ACM, New York, NY, USA. https://doi.org/10.1145/2043932.2043996.

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