Classification, Ranking, and Top-K Stability of Recommendation Algorithms

References

• Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommendation system: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. 17(6):734–749.Crossref, Google Scholar
• Adomavicius G, Zhang J (2010) On the stability of recommendation algorithms. Proc. ACM Conf. Recommender Systems, Barcelona, Spain, 47–54.Crossref, Google Scholar
• Adomavicius G, Zhang J (2011) Maximizing stability of recommendation algorithms: A collective inference approach. Workshop Inform. Tech. Systems, Shanghai, China, 151–156.Google Scholar
• Adomavicius G, Zhang J (2012a) Stability of recommendation algorithms. ACM Trans. Inform. Systems 30(4):Article no. 23.Crossref, Google Scholar
• Adomavicius G, Zhang J (2012b) Impact of data characteristics on recommender systems performance. ACM Trans. Management Inform. Systems 3(1):Article no. 3.Crossref, Google Scholar
• Amatriain X, Basilico J (2012) Netflix recommendations: Beyond the 5 stars. Accessed January 18, 2016, http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html.Google Scholar
• Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: Using social and content-based information in recommendation. Buchanan B, Uthurusamy R, eds. Proc. Conf. Artificial Intelligence/Innovative Appl. Artificial Intelligence, Madison, WI, 714–720.Google Scholar
• Bell RM, Koren Y (2007) Improved neighborhood-based collaborative filtering. KDD Cup Workshop, San Jose, CA, 7–14.Google Scholar
• Bennett J, Lanning S (2007) The Netflix prize. KDD Cup Workshop, San Jose, CA.Google Scholar
• Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proc. 14th Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco), 43–52.Google Scholar
• Christensen D (2005) Fast algorithms for the calculation of Kendall’s τ. Comput. Statist. 20(1):51–62.Crossref, Google Scholar
• Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on Top-N recommendation tasks. Proc. ACM Conf. Recommender Systems (ACM, New York), 39–46.Crossref, Google Scholar
• Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans. Inform. Systems 22(1):143–177.Crossref, Google Scholar
• Fredricks GA, Nelsen RB (2007) On the relationship between Spearman’s rho and Kendall’s tau for pairs of continuous random variables. J. Statist. Planning Inference 137(7):2143–2150.Crossref, Google Scholar
• Funk S (2006) Netflix update: Try this at home. Accessed January 18, 2016, http://sifter.org/∼simon/journal/20061211.html.Google Scholar
• Goldberg K, Roeder T, Gupta D, Perkins C (2001) Eigentaste: A constant time collaborative filtering algorithm. Inform. Retrieval 4(2):133–151.Crossref, Google Scholar
• Goodman LA, Kruskal WH (1954) Measures of association for cross classifications. J. Am. Statist. Assoc. 49(268):732–764.Google Scholar
• GroupLens (2006) MovieLens data sets. Accessed January 18, 2016, http://www.grouplens.org/datasets/movielens.Google Scholar
• Herlocker JL, Konstan JA, Terveen K, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans. Inform. Systems 22(1):5–53.Crossref, Google Scholar
• Hill W, Stead L, Rosenstein M, Furnas G (1995) Recommending and evaluating choices in a virtual community of use. Proc. Conf. Human Factors Comput. Systems (ACM, New York), 194–201.Crossref, Google Scholar
• Kendall M (1938) A new measure of rank correlation. Biometrika 30(1/2):81–93.Crossref, Google Scholar
• Konstan J, Miller B, Maltz D, Herlocker J, Gordon L, Riedl J (1997). GroupLens: Applying collaborative filtering to usenet news. Comm. ACM 40(3):77–87.Crossref, Google Scholar
• Koren Y (2008) Factorization meets the neighborhood: A multifaceted collaborative filtering model. Proc. ACM Internat. Conf. Knowledge Discovery Data Mining, Las Vegas, NV, 426–434.Crossref, Google Scholar
• Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput. Soc. 42(8):30–37.Crossref, Google Scholar
• Lam S, Riedl J (2004) Shilling recommender systems for fun and profit. Proc. 13th Internat. Conf. World Wide Web, New York.Crossref, Google Scholar
• Lemire D, Maclachlan A (2005) Slope one predictors for online rating-based collaborative filtering. Proc. SIAM Data Mining Conf., Newport Beach, CA, 471–475.Crossref, Google Scholar
• Marshall M (2006) Aggregate knowledge raises $5M from Kleiner, on a roll. Accessed January 18, 2016, http://venturebeat.com/2006/12/10/aggregate-knowledge-raises-5m-from-kleiner-on-a-roll/.Google Scholar
• Mitchell T (1997) Machine Learning (McGraw-Hill Education, New York).Google Scholar
• Mobasher B, Burke R, Bhaumik R, Williams C (2007) Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Tech. 7(4):23:21–23:38.Crossref, Google Scholar
• O’Mahony MP, Hurley NJ, Silvestre GCM (2004) An evaluation of neighbourhood formation on the performance of collaborative filtering. Artificial Intelligence Rev. 21(3–4):215–228.Crossref, Google Scholar
• Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994). GroupLens: An open architecture for collaborative filtering of netnews. Proc. Conf. Comput. Supported Cooperative Work, Chapel Hill, NC, 175–186.Crossref, Google Scholar
• Sarwar B, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Proc. 10th Internat. WWW Conf. (ACM, New York), 285–295.Crossref, Google Scholar
• Satzger B, Endres M, Kiessling W (2006) A preference-based recommender system. Proc. 7th Internat. Conf. E-commerce Web Tech. (Springer-Verlag, Berlin), 31–40.Crossref, Google Scholar
• Shani G, Gunawardana A (2011) Evaluating recommendation systems. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 257–294.Crossref, Google Scholar
• Spearman C (1904) The proof and measurement of association between two things. Amer. J. Psych. 15:72–101.Crossref, Google Scholar
• Van Rijsbergen CJ (1979) Information Retrieval, 2nd ed. (Butterworth-Heinemann, Newton, MA).Google Scholar
• Ziegler C, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation list through topic diversification. Proc. 14th Internat. WWW Conf. (ACM, New York), 22–32.Crossref, Google Scholar