Rethinking the recommender research ecosystem: Reproducibility, openness, and LensKit

RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Volumn , Issue , 2011, Pages 133-140

  Ekstrand, Michael D ^a   Ludwig, Michael ^a   Konstan, Joseph A ^a   Riedl, John T ^a  

^a University of Minnesota   (United States)

Author keywords

evaluation; implementation; recommender systems

Indexed keywords

COLLABORATIVE FILTERING ALGORITHMS; COMPARATIVE STUDIES; EVALUATION; EVALUATION FRAMEWORK; EVALUATION METHODOLOGIES; EXPERIMENTAL METHODOLOGY; FLEXIBLE PLATFORMS; IMPLEMENTATION; OFFLINE EVALUATION; ORIGINAL ALGORITHMS; RECOMMENDER ALGORITHMS; REPRODUCIBILITIES; RESEARCH RESULTS; SOURCE CODES; TEST HARNESS;

ALGORITHMS; CODES (SYMBOLS); RESEARCH;

RECOMMENDER SYSTEMS;

EID: 82555195664     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2043932.2043958     Document Type: Conference Paper

References (17)

1. X. Amatriain. Recommender systems: We're doing it (all) wrong. http://technocalifornia.blogspot.com/2011/04/recommender-systems-were-doing-it- all.html, Apr. 2011.
2. N. W. H. Blaikie. Analyzing quantitative data: from description to explanation. SAGE, Mar. 2003.
3. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In UAI 1998, pages 43-52. AAAI, 1998.
4. R. Burke. Evaluating the dynamic properties of recommendation algorithms. In ACM RecSys '10, pages 225-228. ACM, 2010.
5. M. Fowler. Inversion of control containers and the dependency injection pattern. http://martinfowler.com/articles/injection.html, Jan. 2004.
6. S. Funk. Netflix update: Try this at home. http://sifter.org/~simon/ journal/20061211.html, Dec. 2006.
7. M. Hall, E. Frank, G. Holmes, B. Pfahringer, and P. Reutemann. The WEKA data mining software: An update. SIGKDD Explorations, 11(1), 2009.
8. J. Herlocker, J. A. Konstan, and J. Riedl. An empirical analysis of design choices in Neighborhood-Based collaborative filtering algorithms. Inf. Retr., 5(4):287-310, 2002.
9. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5-53, 2004.
10. K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS), 20(4):422-446, Oct. 2002.
11. Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In ACM KDD '08, pages 426-434. ACM, 2008.
12. N. Lathia, S. Hailes, and L. Capra. Evaluating collaborative filtering over time. In SIGIR '09 Workshop on the Future of IR Evaluation, July 2009.
13. J. Levandoski, M. Ekstrand, J. Riedl, and M. Mokbel. RecBench: benchmarks for evaluating performance of recommender system architectures. In VLDB 2011, 2011.
14. A. Paterek. Improving regularized singular value decomposition for collaborative filtering. In KDD Cup and Workshop 2007, Aug. 2007.
15. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. GroupLens: an open architecture for collaborative filtering of netnews. In ACM CSCW '94, pages 175-186. ACM, 1994.
16. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In ACM WWW '01, pages 285-295. ACM, 2001.
17. G. Shani and A. Gunawardana. Evaluating recommendation systems. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 257-297. Springer, 2010.