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Evaluating Recommendation Systems

Shani, Guy, Gunawardana, Asela·2011·Recommender Systems Handbook

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  1. Bamber, D.: The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 12, 387–415 (1975) · DOI · Paper page
  2. Bonhard, P., Harries, C., McCarthy, J., Sasse, M.A.: Accounting for taste: using profile similarity to improve recommender systems. In: CHI ’06: Proceedings of the SIGCHI conference on Human Factors in computing systems, pp. 1057–1066. ACM, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1124772.1124930 · DOI
  3. Boutilier, C., Zemel, R.S.: Online queries for collaborative filtering. In: In Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics (2002)
  4. Box, G.E.P., Hunter,W.G., Hunter, J.S.: Statistics for Experimenters. Wiley, New York (1978)
  5. Bradley, K., Smyth, B.: Improving recommendation diversity. In: Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, pp. 85–94 (2001)
  6. Braziunas, D., Boutilier, C.: Local utility elicitation in GAI models. In: Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, pp. 42–49. Edinburgh (2005)
  7. Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)
  8. Celma, O., Herrera, P.: A new approach to evaluating novel recommendations. In: RecSys ’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 179–186. ACM, New York, NY, USA (2008). DOI http://doi.acm.org/10.1145/1454008.1454038 · DOI
  9. Chirita, P.A., Nejdl, W., Zamfir, C.: Preventing shilling attacks in online recommender systems. In: WIDM ’05: Proceedings of the 7th annual ACM international workshop on Web information and data management, pp. 67–74. ACM, New York, NY, USA (2005). DOI http://doi.acm.org/10.1145/1097047.1097061 · DOI · Paper page
  10. Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW ’07: Proceedings of the 16th international conference on World Wide Web, pp. 271–280. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1242572.1242610 · DOI · Paper page
  11. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
  12. Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004) · DOI · Paper page
  13. Fischer, G.: User modeling in human-computer interaction. User Model. User-Adapt. Interact. 11(1-2), 65–86 (2001) · DOI · Paper page
  14. Fleder, D.M., Hosanagar, K.: Recommender systems and their impact on sales diversity. In: EC ’07: Proceedings of the 8th ACM conference on Electronic commerce, pp. 192–199. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1250910.1250939 · DOI
  15. Frankowski, D., Cosley, D., Sen, S., Terveen, L., Riedl, J.: You are what you say: privacy risks of public mentions. In: SIGIR ’06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 565–572. ACM, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1148170.1148267 · DOI
  16. Fredricks, G.A., Nelsen, R.B.: On the relationship between spearman’s rho and kendall’s tau for pairs of continuous random variables. Journal of Statistical Planning and Inference 137(7), 2143–2150 (2007) · DOI
  17. George, T.: A scalable collaborative filtering framework based on co-clustering. In: Fifth IEEE International Conference on Data Mining, pp. 625–628 (2005) · DOI · Paper page
  18. Greenwald, A.G.: Within-subjects designs: To use or not to use? Psychological Bulletin 83, 216–229 (1976)
  19. Haddawy, P., Ha, V., Restificar, A., Geisler, B., Miyamoto, J.: Preference elicitation via theory refinement. Journal of Machine Learning Research 4, 2003 (2002)
  20. Herlocker, J.L., Konstan, J.A., Riedl, J.T.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287–310 (2002). DOI http://dx.doi.org/10.1023/A:1020443909834 · DOI · Paper page
  21. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004). DOI http://doi.acm.org/10.1145/963770.963772 · DOI · Paper page
  22. Hijikata, Y., Shimizu, T., Nishida, S.: Discovery-oriented collaborative filtering for improving user satisfaction. In: IUI ’09: Proceedings of the 13th international conference on Intelligent user interfaces, pp. 67–76. ACM, New York, NY, USA (2009). DOI http://doi.acm.org/10.1145/1502650.1502663 · DOI
  23. Hu, R., Pu, P.: A comparative user study on rating vs. personality quiz based preference elicitation methods. In: IUI ´09: Proceedings of the 13th international conference on Intelligent user interfaces, pp. 367–372. ACM, New York, NY, USA (2009). DOI http://doi.acm.org/10.1145/1502650.1502702 · DOI
  24. Jones, N., Pu, P.: User technology adoption issues in recommender systems. In: Networking and Electronic Conference (2007)
  25. Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM ’01: Proceedings of the tenth international conference on Information and knowledge management, pp. 247–254. ACM, New York, NY, USA (2001). DOI http://doi.acm.org/10.1145/502585.502627 · DOI · Paper page
  26. Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1–2), 81–93 (1938) · DOI · Paper page
  27. Kendall, M.G.: The treatment of ties in ranking problems. Biometrika 33(3), 239–251 (1945) · DOI · Paper page
  28. Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Discov. 18(1), 140–181 (2009). DOI http://dx.doi.org/10.1007/s10618-008-0114-1 · DOI · Paper page
  29. Konstan, J.A., McNee, S.M., Ziegler, C.N., Torres, R., Kapoor, N., Riedl, J.: Lessons on applying automated recommender systems to information-seeking tasks. In: AAAI (2006)
  30. Koychev, I., Schwab, I.: Adaptation to drifting user’s interests. In: In Proceedings of ECML2000 Workshop: Machine Learning in New Information Age, pp. 39–46 (2000)
  31. Lam, S.K., Frankowski, D., Riedl, J.: Do you trust your recommendations? an exploration of security and privacy issues in recommender systems. In: In Proceedings of the 2006 Interna296 Guy Shani and Asela Gunawardana tional Conference on Emerging Trends in Information and Communication Security (ETRICS (2006) · DOI
  32. Lam, S.K., Riedl, J.: Shilling recommender systems for fun and profit. In: WWW ’04: Proceedings of the 13th international conference on World Wide Web, pp. 393–402. ACM, New York, NY, USA (2004). DOI http://doi.acm.org/10.1145/988672.988726 · DOI · Paper page
  33. Mahmood, T., Ricci, F.: Learning and adaptivity in interactive recommender systems. In: ICEC ’07: Proceedings of the ninth international conference on Electronic commerce, pp. 75–84. ACM, New York, NY, USA (2007). DOI http://doi.acm.org/10.1145/1282100.1282114 · DOI
  34. Marlin, B.M., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. In: Proceedings of the 23rd COnference on Uncertainity in Artificial Intelligence (2007)
  35. Massa, P., Bhattacharjee, B.: Using trust in recommender systems: An experimental analysis. In: In Proceedings of iTrust2004 International Conference, pp. 221–235 (2004) · DOI · Paper page
  36. McLaughlin, M.R., Herlocker, J.L.: A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In: SIGIR ’04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 329–336. ACM, New York, NY, USA (2004). DOI http://doi.acm.org/10.1145/1008992.1009050 · DOI
  37. McNee, S.M., Riedl, J., Konstan, J.A.: Making recommendations better: an analytic model for human-recommender interaction. In: CHI ’06: CHI ’06 extended abstracts on Human factors in computing systems, pp. 1103–1108. ACM, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1125451.1125660 · DOI
  38. McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the netflix prize contenders. In: KDD ’09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 627–636. ACM, New York, NY, USA (2009). DOI http://doi.acm.org/10.1145/1557019.1557090 · DOI · Paper page
  39. Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7(4), 23 (2007). DOI http://doi.acm.org/10.1145/1278366.1278372 · DOI · Paper page
  40. Murakami, T., Mori, K., Orihara, R.: Metrics for evaluating the serendipity of recommendation lists. New Frontiers in Artificial Intelligence 4914, 40–46 (2008) · DOI
  41. O’Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4(4), 344–377 (2004). DOI http://doi.acm.org/10.1145/1031114.1031116 · DOI · Paper page
  42. Pfleeger, S.L., Kitchenham, B.A.: Principles of survey research. SIGSOFT Softw. Eng. Notes 26(6), 16–18 (2001). DOI http://doi.acm.org/10.1145/505532.505535 · DOI · Paper page
  43. Queiroz, S.: Adaptive preference elicitation for top-k recommendation tasks using gainetworks. In: AIAP’07: Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference, pp. 579–584. ACTA Press, Anaheim, CA, USA (2007)
  44. Salzberg, S.L.: On comparing classifiers: Pitfalls toavoid and a recommended approach. Data Min. Knowl. Discov. 1(3), 317–328 (1997). DOI http://dx.doi.org/10.1023/A:1009752403260 · DOI · Paper page
  45. Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In:WWW’01: Proceedings of the 10th international conference onWorld Wide Web, pp. 285–295. ACM, New York, NY, USA (2001). DOI http://doi.acm.org/10.1145/371920.372071 · DOI · Paper page
  46. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: EC ’00: Proceedings of the 2nd ACM conference on Electronic commerce, pp. 158–167. ACM, New York, NY, USA (2000). DOI http://doi.acm.org/10.1145/352871.352887 · DOI · Paper page
  47. Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR ’02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253–260. ACM, New York, NY, USA (2002). DOI http://doi.acm.org/10.1145/564376.564421 · DOI · Paper page
  48. Shani, G., Chickering, D.M., Meek, C.: Mining recommendations from the web. In: RecSys ’08: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 35–42 (2008) · DOI
  49. Shani, G., Heckerman, D., Brafman, R.I.: An mdp-based recommender system. Journal of Machine Learning Research 6, 1265–1295 (2005)
  50. Smyth, B., McClave, P.: Similarity vs. diversity. In: ICCBR, pp. 347–361 (2001) · DOI · Paper page
  51. Swearingen, K., Sinha, R.: Beyond algorithms: An hci perspective on recommender systems. In: ACM SIGIR 2001 Workshop on Recommender Systems (2001)
  52. Van Rijsbergen, C.J.: Information Retrieval. Butterworth-Heinemann, Newton, MA, USA (1979). URL http://portal.acm.org/citation.cfm?id=539927
  53. Voorhees, E.M.: Overview of trec 2002. In: In Proceedings of the 11th Text Retrieval Conference (TREC 2002), NIST Special Publication 500-251, pp. 1–15 (2002) · DOI
  54. Voorhees, E.M.: The philosophy of information retrieval evaluation. In: CLEF ’01: Revised Papers from the Second Workshop of the Cross-Language Evaluation Forum on Evaluation of Cross-Language Information Retrieval Systems, pp. 355–370. Springer-Verlag, London, UK (2002) · DOI · Paper page
  55. Yao, Y.Y.: Measuring retrieval effectiveness based on user preference of documents. J. Amer. Soc. Inf. Sys 46(2), 133–145 (1995) · DOI
  56. Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: RecSys ’08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 123–130. ACM, New York, NY, USA (2008). DOI http://doi.acm.org/10.1145/1454008.1454030 · DOI · Paper page
  57. Zhang, Y., Callan, J., Minka, T.: Novelty and redundancy detection in adaptive filtering. In: SIGIR ’02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 81–88. ACM, New York, NY, USA (2002).DOI http://doi.acm.org/10.1145/564376.564393 · DOI · Paper page
  58. Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW ´05: Proceedings of the 14th international conference on World Wide Web, pp. 22–32. ACM, New York, NY, USA (2005). DOI http://doi.acm.org/10.1145/1060745.1060754 · DOI · Paper page

Cited by

Metadata

Authors
Shani, G., & Gunawardana, A.
Venue
Recommender Systems Handbook
Publisher
Springer US
Published
2010-10-05
Pages
257-297
OpenAlex
W2108630796
Cited by
1,325
References
5
FWCI
126.85
Type
book chapter
Language
en
Open access
closed
Licence

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