ABSTRACT
With the increasing ubiquity of access to online news sources, the news recommender systems are becoming widely popular in recent days. However, providing interesting news for each user is a challenging task in highly-dynamic news domain. Many news aggregator sites such as Google News suggest its users to provide sign in to the system for getting user-specific (relevant) news articles. For more generic news recommendation, the system collects user click history and page access pattern implicitly. Often the users are not sure about the usage of the collected and consolidated data by the recommender systems which they usually trade for receiving the news recommendation. Privacy of user identity, user behavior in terms of page access patterns contributes to the overall privacy risks in the news domain. This review paper discusses the current state-of-the-art of privacy risks and existing privacy preserving approaches in the news domain from user perspective.
- Adomavicius, G. and Tuzhilin, A., 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6, 734--749. Google ScholarDigital Library
- Aggarwal, C.C., 2016. Recommender Systems: The Textbook. Springer Publishing Company, Incorporated. Google ScholarDigital Library
- Berkovsky, S., Eytani, Y., Kuflik, T., and Ricci, F., 2005. Privacy-enhanced collaborative filtering. In Proceedings of User Modeling Workshop on Privacy-Enhanced Personalization, 75--83.Google Scholar
- Braunhofer, M., Codina, V., and Ricci, F., 2014. Switching hybrid for cold-starting context-aware recommender systems. In Proceedings of the Proceedings of the 8th ACM Conference on Recommender systems (Foster City, Silicon Valley, California, USA2014),ACM,2645757,349--352. Google ScholarDigital Library
- Ciss, R. and Albayrak, S., 2007. An agent-based approach for privacy-preserving recommender systems. In Proceedings of the Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems (Honolulu, Hawaii2007), ACM, 1329345, 1--8. Google ScholarDigital Library
- Cranor, L., Langheinrich, M., Marchiori, M., Martin Presler-Marshall, and Reagle, J., 2002. The Platform for Privacy Preferences 1.0 (P3P1.0) Specification 2017. https://www.w3.org/TR/P3P/.Google Scholar
- Das, A.S., Datar, M., Garg, A., and Rajaram, S., 2007. Google news personalization: scalable online collaborative filtering. In Proceedings of the Proceedings of the 16th international conference on World Wide Web (Banff, Alberta, Canada2007), ACM, 1242610, 271--280. Google ScholarDigital Library
- Desarkar, M.S. and Shinde, N., 2014. Diversification in news recommendation for privacy concerned users. In 2014 International Conference on Data Science and Advanced Analytics (DSAA), 135--141.Google Scholar
- Doychev, D., Lawlor, A., and Rafter, R., 2014. An Analysis of Recommender Algorithms for Online News. CLEF.Google Scholar
- Erkin, Z., Veugen, T., and Lagendijk, R.L., 2013. Privacy-preserving recommender systems in dynamic environments. In 2013 IEEE International Workshop on Information Forensics and Security (WIFS), 61--66.Google Scholar
- European Commission, 2016. The EU-U.S. Privacy Shield 2017,, June 23, . http://ec.europa.eu/justice/data-protection/international-transfers/eu-us-privacy-shield/index_en.htm.Google Scholar
- European Commission, 2016. Reform of EU data protection rules 2017, June 23. http://ec.europa.eu/justice/data-protection/reform/index_en.htm.Google Scholar
- Friedman, A., Knijnenburg, B.P., Vanhecke, K., Martens, L., and Berkovsky, S., 2015. Privacy Aspects of Recommender Systems. In Recommender Systems Handbook, F. Ricci, L. Rokach and B. Shapira Eds. Springer US, Boston, MA, 649--688.Google Scholar
- Garcin, F. and Faltings, B., 2013. PEN recsys: a personalized news recommender systems framework. In Proceedings of the Proceedings of the 2013 International News Recommender Systems Workshop and Challenge (Kowloon, Hong Kong2013), ACM, 2516642, 3--9. Google ScholarDigital Library
- Gulla, J.A., Fidjestøl, A.D., Su, X., and Martínez, H.N.C., 2014. Implicit User Profiling in News Recommender Systems. In Proceedings of the 10th International Conference on Web Information Systems and TechnologiesGoogle Scholar
- Hansen, M., 2008. Marrying Transparency Tools with User-Controlled Identity Management. In The Future of Identity in the Information Society: Proceedings of the Third IFIP WG 9.2, 9.6/11.6, 11.7/FIDIS International Summer School on The Future of Identity in the Information Society, Karlstad University, Sweden, August 4--10, 2007, S. Fischer-Hübner, P. Duquenoy, A. Zuccato and L. Martucci Eds. Springer US, Boston, MA, 199--220.Google Scholar
- Ilievski, I. and Roy, S., 2013. Personalized news recommendation based on implicit feedback. In Proceedings of the Proceedings of the 2013 International News Recommender Systems Workshop and Challenge (Kowloon, Hong Kong2013), ACM, 2516644, 10--15. Google ScholarDigital Library
- Ingvaldsen, J.E., Gulla, J.A., and Özgöbek, Ö., 2015. User Controlled News Recommendations. In IntRS@RecSys, 45--48.Google Scholar
- Ingvaldsen, J.E., Özgöbek, Ö., and Gulla, J.A., 2015. Context-Aware User-Driven News Recommendation. In INRA@RecSys.Google Scholar
- Jeckmans, A.J.P., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L., and Tang, Q., 2013. Privacy in Recommender Systems. In Social Media Retrieval, N. Ramzan, R. Van Zwol, J.-S. Lee, K. Clüver and X.-S. Hua Eds. Springer London, London, 263--281.Google Scholar
- Lam, S.K.T., Frankowski, D., and Riedl, J., 2006. Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems. In Emerging Trends in Information and Communication Security: International Conference, ETRICS 2006, Freiburg, Germany, June 6--9, 2006. Proceedings, G. Müller Ed. Springer Berlin Heidelberg, Berlin, Heidelberg, 14--29. Google ScholarDigital Library
- Li, L., Wang, D.-D., Zhu, S.-Z., and Li, T., 2011. Personalized News Recommendation: A Review and an Experimental Investigation. Journal of Computer Science and Technology 26, 5, 754--766. Google ScholarDigital Library
- Liu, J., Dolan, P., and Pedersen, E.R., 2010. Personalized news recommendation based on click behavior, 31.Google Scholar
- Mcsherry, F. and Mironov, I., 2009. Differentially private recommender systems: building privacy into the net. In Proceedings of the Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (Paris, France2009), ACM, 1557090, 627--636. Google ScholarDigital Library
- Narayanan, A. and Shmatikov, V., 2008. Robust Deanonymization of Large Sparse Datasets. In Proceedings of the Proceedings of the 2008 IEEE Symposium on Security and Privacy (2008), IEEE Computer Society, 1398064, 111--125. Google ScholarDigital Library
- Pariser, E., 2012. The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin Books. Google ScholarDigital Library
- Polat, H. and Wenliang, D., 2003. Privacy-preserving collaborative filtering using randomized perturbation techniques. In Third IEEE International Conference on Data Mining, 625--628. Google ScholarDigital Library
- Ramakrishnan, N., Keller, B.J., Mirza, B.J., Grama, A.Y., and Karypis, G., 2001. Privacy Risks in Recommender Systems. IEEE Internet Computing 5, 6, 54--62. Google ScholarDigital Library
- Ricci, F., Rokach, L., Shapira, B., and Kantor, P.B., 2010. Recommender Systems Handbook. Springer-Verlag New York, Inc. Google ScholarDigital Library
- Shokri, R., Pedarsani, P., Theodorakopoulos, G., and Hubaux, J.-P., 2009. Preserving privacy in collaborative filtering through distributed aggregation of offline profiles. In Proceedings of the Proceedings of the third ACM conference on Recommender systems (New York, New York, USA2009), ACM, 1639741, 157--164. Google ScholarDigital Library
- Tintarev, N., 2007. Explanations of recommendations. In Proceedings of the Proceedings of the 2007 ACM conference on Recommender systems (Minneapolis, MN, USA2007), ACM, 1297275, 203--206. Google ScholarDigital Library
- Verykios, V.S., Bertino, E., Fovin, I.N., Provenza, L.P., Saygin, Y., and Theodoridis, Y., 2004. State-of-the-art in privacy preserving data mining. Sigmod Record 33, 1 (Mar), 50--57. Google ScholarDigital Library
- Walton, D., 1996. Plausible deniability and evasion burden of proof. Argumentation. Argumentation 10, 10--47.Google ScholarCross Ref
- Wen, H., Fang, L., and Guan, L., 2012. A hybrid approach for personalized recommendation of news on the Web. Expert Systems with Applications 39, 5 (4//), 5806--5814. DOI= http://dx.doi.org/ Google ScholarDigital Library
- Yunseok, N., Yong-Hwan, O., and Seong-Bae, P., 2014. A location-based personalized news recommendation. In 2014 International Conference on Big Data and Smart Computing (BIGCOMP), 99--104.Google Scholar
- Zhu, X. and Hao, R., 2016. Context-aware location recommendations with tensor factorization. In IEEE/CIC International Conference on Communications in China (ICCC) IEEE, Chengdu, China 1--6.Google Scholar
- Özgöbek, Ö., Gulla, J.A., and Erdur, R.C., 2014. A Survey on Challenges and Methods in News Recommendation. In Proceedings of the 10th International Conference on Web Information Systems and Technologies :WEBIST, 278--285.Google Scholar
Recommendations
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data MiningRecommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied ComputingOver the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...
Comments