Incorporating Aggregate Diversity in Recommender Systems Using Scalable Optimization Approaches

References

• Adomavicius G, Kwon Y (2012) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5):896–911.Crossref, Google Scholar
• Adomavicius G, Kwon Y (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J. Comput. 26(2):351–369.Link, Google Scholar
• Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6):734–749.Crossref, Google Scholar
• Aytekin T, Karakaya MÖ (2014) Clustering-based diversity improvement in top-n recommendation. J. Intell. Inf. Syst. 42(1):1–18.Crossref, Google Scholar
• Bradley K, Smyth B (2001) Improving recommendation diversity. O’Donoghue D, ed. Proc. 12th Irish Conf. Artificial Intelligence Cognitive Sci., Ireland, 85–94.Google Scholar
• Chankong V, Haimes YY (1983) Multiobjective Decision Making Theory and Methodology (Elsevier Science, New York).Google Scholar
• Dantzig GB, Wolfe P (1960) Decomposition principle for linear programs. Oper. Res. 8(1):101–111.Link, Google Scholar
• Desrosiers C, Karypis G (2011) A comprehensive survey of neighborhood-based recommendation methods. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 107–144.Crossref, Google Scholar
• Ehrgott M (2005) Multicriteria Optimization. Lecture Notes in Economics and Mathematical Systems (Springer-Verlag, Berlin).Google Scholar
• Ehrgott M (2006) A discussion of scalarization techniques for multiple objective integer programming. Ann. Oper. Res. 147(1):343–360.Crossref, Google Scholar
• Ehrgott M, Ryan DM (2002) Constructing robust crew schedules with bicriteria optimization. J. Multi-Criteria Decision Anal. 11(3):139–150.Crossref, Google Scholar
• Fisher LM (1981) The Lagrangean relaxation method for solving integer programming problems. Management Sci. 27(1):1–18.Link, Google Scholar
• Fleder DM, Hosanagar K (2009) Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity. Management Sci. 55(5):697–712.Link, Google Scholar
• Gilmore PC, Gomory RE (1961) A linear programming approach to the cutting-stock problem. Oper. Res. 9(6):849–859.Link, Google Scholar
• Goffin J-L, Vial J-P (2002) Convex nondifferentiable optimization: A survey focused on the analytic center cutting plane method. Optim. Methods and Software 17(5):805–867.Crossref, Google Scholar
• Goldstein DG, Goldstein DC (2006) Profiting from the long tail. Harvard Bus. Rev. 84(6):24–28.Google Scholar
• Held M, Karp RM (1970) The travelling salesman problem and minimum spanning trees: Part i. Oper. Res. 18:1138–1162.Link, Google Scholar
• Held M, Karp RM (1971) The travelling salesman problem and minimum spanning trees: Part ii. Math. Programming 1(1):6–25.Crossref, Google Scholar
• Held M, Wolfe P, Crowder HP (1974) Validation of subgradient optimization. Math. Programming 6(1):62–88.Crossref, Google Scholar
• Herlocker JL, Konstan JA, Terveen LG, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1):5–53.Crossref, Google Scholar
• Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback data sets. Proc. 8th IEEE Internat. Conf. Data Mining (ICDM 2008), Pisa, Italy, 263–272.Google Scholar
• Hurley N, Zhang M (2011) Novelty and diversity in top-n recommendation—Analysis and evaluation. ACM Trans. Internet Techn. 10(4):14:1–14:30.Crossref, Google Scholar
• Koren Y, Bell RM (2011) Advances in collaborative filtering. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 145–186.Crossref, Google Scholar
• Lemaréchal C, Strodiot JJ, Bihain A (1981) On a bundle algorithm for nonsmooth optimization. Mangasarian OL, Meyer RR, Robinson SM, eds. Nonlinear Programming, Chap. 4 (Academic Press, New York), 331–358.Crossref, Google Scholar
• Lokman B, Köksalan M (2013) Finding all nondominated points of multi-objective integer programs. J. Global Optim. 57(2):347–365.Crossref, Google Scholar
• Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: State of the art and trends. Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook (Springer, New York), 73–105.Crossref, Google Scholar
• Lübbecke ME, Desrosiers J (2005) Selected topics in column generation. Oper. Res. 53(6):1007–1023.Link, Google Scholar
• McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. Olson GM, Jeffries R, eds. CHI Extended Abstracts (ACM, New York), 1097–1101.Crossref, Google Scholar
• Özlen M, Azizoğlu M (2009) Multi-objective integer programming: A general approach for generating all non-dominated solutions. Eur. J. Oper. Res. 199(1):25–35.Crossref, Google Scholar
• Ricci F, Rokach L, Shapira B, Kantor PB, eds. (2011) Recommender Systems Handbook (Springer, New York).Crossref, Google Scholar
• Smyth B, McClave P (2001) Similarity vs. diversity. Aha DW, Watson I, eds. Proc. 4th Internat. Conf. Case-Based Reasoning, Vol. 2080, Lecture Notes in Computer Science (Springer, Vancouver, Canada), 347–361.Crossref, Google Scholar
• Sylva J, Crema A (2004) A method for finding the set of non-dominated vectors for multiple objective integer linear programs. Eur. J. Oper. Res. 158(1):46–55.Crossref, Google Scholar
• Wu L, Shah S, Choi S, Tiwari M, Posse C (2014) The browsemaps: Collaborative filtering at LinkedIn. Jannach D, Freyne J, Geyer W, Guy I, Hotho A, Mobasher B, eds. Proc. 6th Workshop Recommender Systems Social Web (ACM, New York).Google Scholar
• Ziegler C-N, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. Proc. 14th Internat. Conf. World Wide Web, Chiba, Japan (ACM, New York) 22–32.Crossref, Google Scholar