Evaluating recommender behavior for new users

RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems

Volumn , Issue , 2014, Pages 121-128

  Kluver, Daniel ^a   Konstan, Joseph A ^a  

^a University of Minnesota   (United States)

Author keywords

Evaluation; New user experience; New user problem; Profile size; Recommender systems; User cold start

Indexed keywords

COLLABORATIVE FILTERING; RECOMMENDER SYSTEMS;

COLD START; COLLABORATIVE FILTERING ALGORITHMS; EVALUATION; NEW USER PROBLEMS; PROFILE SIZE;

USER EXPERIENCE;

EID: 84908879792     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2645710.2645742     Document Type: Conference Paper

References (18)

1. A. Bellogín. Performance prediction and evaluation in Recommender Systems: An Information Retrieval perspective. PhD thesis, Universidad Autónoma de Madrid, Madrid, Spain, Oct. 2012.
2. P. Cremonesi, F. Garzottto, and R. Turrin. User effort vs. accuracy in rating-based elicitation. In RecSys '12. ACM, 2012.
3. P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In RecSys '10. ACM, 2010.
4. S. Drenner, S. Sen, and L. Terveen. Crafting the initial user experience to achieve community goals. In RecSys '08. ACM, 2008.
5. M. Ekstrand and J. Riedl. When recommenders fail: Predicting recommender failure for algorithm selection and combination. In RecSys '12. ACM, 2012.
6. M. D. Ekstrand, M. Ludwig, J. A. Konstan, and J. T. Riedl. Rethinking the recommender research ecosystem: Reproducibility, openness, and lenskit. In RecSys '11. ACM, 2011.
7. S. Funk. Netflix update: Try this at home. http://sifter.org/simon/journal/20061211.html, Dec. 2006.
8. N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In WSDM '11. ACM, 2011.
9. N. Golbandi, Y. Koren, and R. Lempel. Adaptive bootstrapping of recommender systems using decision trees. In WSDM '11. ACM, 2011.
10. 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, Jan. 2004.
11. N. Lathia, S. Hailes, L. Capra, and X. Amatriain. Temporal diversity in recommender systems. In SIGIR '10. ACM, 2010.
12. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval, volume 1. Cambridge university press Cambridge, 2008.
13. S. M. McNee, J. Riedl, and J. A. Konstan. Being accurate is not enough: How accuracy metrics have hurt recommender systems. In CHI EA '06. ACM, 2006.
14. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In CSCW '94. ACM, 1994.
15. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW '01. ACM, 2001.
16. 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 US, 2011.
17. M. C. Willemsen, B. P. Knijnenburg, M. P. Graus, L. C. Velter-Bremmers, and K. Fu. Using latent features diversification to reduce choice difficulty in recommendation lists. RecSys '11, 2011.
18. C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen. Improving recommendation lists through topic diversification. In WWW '05. ACM, 2005.