Manipulation Robustness of Collaborative Filtering

Published Online:https://doi.org/10.1287/mnsc.1100.1232

References

  • Adomavicius G., Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Engrg. (2005) 17(6):734–749CrossrefGoogle Scholar
  • Balabanovic M., Shoham Y. Fab: Content-based, collaborative recommendation. Comm. ACM (1997) 40(3):66–72CrossrefGoogle Scholar
  • Bennett J. The Cinematch system: Operation, scale coverage, accuracy impact. (2006) . Accessed January 2009, http://blog.recommenders06.com/wp-content/uploads/2006/09/1jimbennett.wmvGoogle Scholar
  • Bhattacharjee R., Goel A. Algorithms and incentives for robust ranking. SODA '07: Proc. Eighteenth Annual ACM-SIAM Sympos. Discrete Algorithms (2007) (Society for Industrial and Applied Mathematics, Philadelphia) 425–433Google Scholar
  • Breese J., Heckerman D., Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Proc. Fourteenth Annual Conf. Uncertainty in Artificial Intelligence (1998) (Morgan Kaufmann, Madison, WI) Google Scholar
  • Burke R., Mobasher B., Zabicki R., Bhaumik R. Limited knowledge shilling attacks in collaborative filtering systems. Proc. Third IJCAI Workshop in Intelligent Techniques for Personalization (2005) (Springer-Verlag, New York) Google Scholar
  • Cheeseman P., Stutz J., Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. Bayesian classification (AutoClass): Theory and results. Advances in Knowledge Discovery and Data Mining (1996) (AAAI Press, Menlo Park, CA) 153–180Google Scholar
  • Dellarocas C. Strategic manipulation of Internet opinion forums: Implications for consumers and firms. Management Sci. (2006) 52(10):1577–1593LinkGoogle Scholar
  • Dempster A., Laird N., Rubin D. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. (1977) 39(1):1–38Google Scholar
  • Domingos P., Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learn. (1997) 29(2):103–130CrossrefGoogle Scholar
  • Drineas P., Kerenidis I., Raghavan P. Competitive recommendation systems. STOC '02: Proc. Thirty-Fourth Annual ACM Sympos. Theory Comput. (2002) (ACM, New York) 82–90CrossrefGoogle Scholar
  • Friedman E., Resnick P., Sami R., Nisan N., Roughgarden T., Tardos É., Vazirani V. V. Manipulation-resistant reputation systems. Algorithmic Game Theory (2007) (Cambridge University Press, New York) 677–697Chap. 27CrossrefGoogle Scholar
  • Gossner O., Tomala T. Entropy bounds on Bayesian learning. J. Math. Econom. (2008) 44(1):24–32CrossrefGoogle Scholar
  • Herlocker J., Konstan J., Borchers A., Riedl J. An algorithmic framework for performing collaborative filtering. SIGIR'99: Proc. Twenty-Second Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (1999) (ACM, New York) 230–237CrossrefGoogle Scholar
  • John G., Langley P. Estimating continuous distributions in Bayesian classifiers. Proc. Eleventh Conf. Uncertainty in Artificial Intelligence (1995) (Morgan Kaufmann, San Mateo, CA) 338–345Google Scholar
  • Kleinberg J., Sandler M. Using mixture models for collaborative filtering. Comput. System Sci. (2008) 74(1):49–69CrossrefGoogle Scholar
  • Lam S., Riedl J. Shilling recommender systems for fun and profit. WWW '04: Proc. Thirteenth Internat. Conf. World Wide Web (2004) (ACM, New York) 393–402CrossrefGoogle Scholar
  • Linden G., Smith B., York J. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet. Comput. (2003) 7(1):76–80CrossrefGoogle Scholar
  • Massa P., Avesani P. Trust-aware collaborative filtering for recommender systems. Lecture Notes in Computer Science (2004) 3290(Springer-Verlag, Berlin) 492–508CrossrefGoogle Scholar
  • Mehta B. Unsupervised shilling detection for collaborative filtering. AAAI '07: Proc. Twenty-Second National Conf. Artificial Intelligence (2007) (AAAI Press, Menlo Park, CA) 1402–1407Google Scholar
  • Mehta B., Nejdl W. Attack resistant collaborative filtering. SIGIR '08 Proc. Thirty-First Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (2008) (ACM, New York) 75–82CrossrefGoogle Scholar
  • Miller N., Resnick P., Zeckhauser R. Eliciting informative feedback: The peer-prediction method. Management Sci. (2005) 51(9):1359–1373LinkGoogle Scholar
  • Mobasher B., Burke R., Sandvig J. J. Model-based collaborative filtering as a defense against profile injection attacks. Proc. Twenty-First National Conf. Artificial Intelligence (2006) (AAAI Press, Menlo Park, CA) 1388–1393Google Scholar
  • Mobasher B., Burke R., Bhaumik R., Williams C. Effective attack models for shilling item-based collaborative filtering systems. Proc. 2005 WebKDD Workshop (2005) (ACM, New York) 13–23Google Scholar
  • Mobasher B., Burke R., Bhaumik R., Williams C. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Tech. (2007) 7(4). Article no. 23CrossrefGoogle Scholar
  • Moon S., Russell G. Predicting product purchase from inferred customer similarity: An autologistic model approach. Management Sci. (2008) 54(1):71–82LinkGoogle Scholar
  • Motwani R., Vassilvitskii S. Tracing the path: New model and algorithms for collaborative filtering. Twenty-Third Internat. Conf. Data Engrg. Workshop (2007) (IEEE, Computer Society, Washington, DC) 853–862CrossrefGoogle Scholar
  • Netflix Prize (2006) . Accessed October 2008, http://www.netflixprize.comGoogle Scholar
  • O'Donovan J., Smyth B. Is trust robust? An analysis of trust-based recommendation. IUI '06: Proc. Eleventh Internat. Conf. Intelligent User Interfaces (2006) (ACM, New York) 101–108CrossrefGoogle Scholar
  • Olsen S. Amazon blushes over sex link gaffe. CNET News (2002) December 6). http://news.cnet.com/2100-1023-976435.htmlGoogle Scholar
  • O'Mahony M., Hurley N., Kushmerick N., Silvestre G. Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technologies (2004) 4(4):344–377CrossrefGoogle Scholar
  • Resnick P., Sami R. The influence limiter: Provably manipulation-resistant recommender systems. RecSys '07: Proc. ACM Conf. Recommender Systems (2007) (ACM, New York) 25–32CrossrefGoogle Scholar
  • Resnick P., Sami R. The information cost of manipulation-resistance in recommender systems. RecSys '08: Proc. 2008 ACM Conf. Recommender Systems (2008) (ACM, New York) 147–154CrossrefGoogle Scholar
  • Ryan L. (2008) . Interview by Xiang Yan, San FranciscoGoogle Scholar
  • Sandvig J. J., Mobasher B., Burke R. Robustness of collaborative recommendation based on association rule mining. RecSys '07: Proc. 2007 ACM Conf. Recommender Systems (2007) (ACM, New York) 105–112CrossrefGoogle Scholar
  • Sarwar B., Karypis G., Konstan J., Reidl J. Item-based collaborative filtering recommendation algorithms. WWW '01: Proc. Tenth Internat. Conf. World Wide Web (2001) (ACM, New York) 285–295CrossrefGoogle Scholar
  • Schafer J. B., Konstan J., Riedl J. E-Commerce recommendation applications. Data Mining and Knowledge Discovery (2001) 5(1):115–153CrossrefGoogle Scholar
  • Van Roy B., Yan X. Manipulation-resistant collaborative filtering systems. RecSys '09: Proc. Third ACM Conf. Recommender Systems (2009) (ACM, New York) 165–172CrossrefGoogle Scholar
  • Wald A. Note on the consistency of the maximum likelihood estimate. Ann. Math. Statist. (1949) 20(4):595–601CrossrefGoogle Scholar
  • Williams C., Mobasher B., Burke R. Defending recommender systems: Detection of profile injection attacks. J. Service Oriented Comput. Appl. (2007) 1(3):157–170CrossrefGoogle Scholar
  • Zhang S., Ouyang Y., Ford J., Makedon F. Analysis of a low-dimensional linear model under recommendation attacks. SIGIR '06: Proc. Twenty-Ninth Annual Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (2006) (ACM, New York) 517–524CrossrefGoogle Scholar
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