Matrix factorization without user data retention

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8443 LNAI, Issue PART 1, 2014, Pages 569-580

  Vallet, David ^a   Friedman, Arik ^a   Berkovsky, Shlomo ^a  

^a CSIRO   (Australia)

Author keywords

data retention; matrix factorization; recommender systems

Indexed keywords

DATA MINING; RECOMMENDER SYSTEMS;

DATA RETENTION; EXPERIMENTAL EVALUATION; MATRIX FACTORIZATIONS; SENSITIVE INFORMATIONS; USER DATA; USER RATING;

DIGITAL STORAGE;

EID: 84901276301     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-06608-0_47     Document Type: Conference Paper

References (20)

1. Berkovsky, S., Eytani, Y., Kuflik, T., Ricci, F.: Enhancing privacy and preserving accuracy of a distributed collaborative filtering. In: RecSys, pp. 9-16 (2007)
2. Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: "You might also like:" privacy risks of collaborative filtering. In: IEEE Symposium on Security and Privacy, pp. 231-246 (2011)
3. Campos, P., Díez, F., Cantador, I.: Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. In: UMUAI (2013)
4. Chaabane, A., Acs, G., Kaafar, M.: You are what you like! information leakage through users' interests. In: Proc. NDSS (2012)
5. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys, pp. 39-46 (2010)
6. Greenwald, G., MacAskill, E.: NSA Prism program taps in to user data of Apple, Google and others. In: The Guardian (June 2013)
7. Guha, S., Reznichenko, A., Tang, K., Haddadi, H., Francis, P.: Serving ads from localhost for performance, privacy, and profit. In: HotNets (2009)
8. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Computer 42(8), 30-37 (2009)
9. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences (March 2013)
10. Lam, S.K., Frankowski, D., Riedl, J.: Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Müller, G. (ed.) ETRICS 2006. LNCS, vol. 3995, pp. 14-29. Springer, Heidelberg (2006)
11. Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. Society for Industrial Mathematics 5, 471-480 (2005)
12. Luo, X., Xia, Y., Zhu, Q.: Incremental collaborative filtering recommender based on regularized matrix factorization. Know. -Based Syst. 27, 271-280 (2012)
13. McSherry, F., Mironov, I.: Differentially private recommender systems: Building privacy into the netflix prize contenders. In: KDD, pp. 627-636 (2009)
14. Narayanan, A., Shmatikov, V.: Robust de-anonymization of large sparse datasets. In: IEEE Symposium on Security and Privacy, pp. 111-125 (2008)
15. Rendle, S., Schmidt-Thieme, L.: Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: RecSys (2008)
16. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Incremental singular value decomposition algorithms for highly scalable recommender systems. In: Fifth International Conference on Computer and Information Science, pp. 27-28 (2002)
17. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook, pp. 257-297. Springer (2011)
18. Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. JMLR 10, 623-656 (2009)
19. Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H., Barocas, S.: Adnostic: Privacy preserving targeted advertising. In: NDSS (2010)
20. Weinsberg, U., Bhagat, S., Ioannidis, S., Taft, N.: BlurMe: inferring and obfuscating user gender based on ratings. In: RecSys, pp. 195-202 (2012)