Abstract
In this paper, we introduce privacy-preserving query personalization for experience items like movies, music, games or books. While these items are rather common, describing them with semantically meaningful attribute values is challenging, thus hindering traditional database query personalization. This often leads to the use of recommender systems, which, however, have several drawbacks as for example high barriers for new users joining the system, the inability to process dynamic queries, and severe privacy concerns due to requiring extensive long-term user profiles. We propose an alternative approach, representing experience items in a perceptual space using high-dimensional and semantically rich features. In order to query this space, we provide query-by-example personalization relying on the perceived similarity between items, and learn a user’s current preferences with respect to the query on the fly. Furthermore, for query execution, our approach addresses privacy issues of recommender systems as we do not require user profiles for queries, do not leak any personal information during interaction, and allow users to stay anonymous while querying. In this paper, we provide the foundations of such a system and then extensively discuss and evaluate the performance of our approach under different assumptions. Also, suitable optimizations and modifications to ensure scalability on current hardware are presented.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bell, R.M., Koren, Y., Volinsky, C.: All together now: A perspective on the Netflix Price. Chance 23, 24–24 (2010)
Koren, Y., Bell, R.: Advances in Collaborative Filtering. In: Recommender Systems Handbook, pp. 145–186 (2011)
Selke, J., Lofi, C., Balke, W.-T.: Pushing the Boundaries of Crowd-Enabled Databases with Query-Driven Schema Expansion. In: Proc. VLDB, vol. 5, pp. 538–549 (2012)
Pariser, E.: The Filter Bubble: What the Internet Is Hiding from You. Penguin Books (2011)
Sundaram, H., Chang, S.-F.: Computable scenes and structures in films. IEEE Trans. Multimed. 4, 482–491 (2002)
Neo, S.-Y., Zhao, J., Kan, M.-Y., Chua, T.-S.: Video Retrieval Using High Level Features: Exploiting Query Matching and Confidence-Based Weighting. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds.) CIVR 2006. LNCS, vol. 4071, pp. 143–152. Springer, Heidelberg (2006)
Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: ACM Conf. on Recom. Systems (RecSys), New York, USA (2009)
Kahneman, D., Tversky, A.: Psychology of Preferences. Sci. Am. 246, 160–173 (1982)
Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. UMUAI 22, 441–504 (2012)
Narayanan, A., Shmatikov, V.: Robust De-anonymization of Large Sparse Dataset. In: IEEE Symposium on Security and Privacy, Oakland, USA (2008)
Yoshitaka, A., Lchikawa, T.: A survey on content-based retrieval for multimedia databases. IEEE Trans. Knowl. Data Eng. 11 (1999)
Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color image database-query by visual example. Pattern Recognit. (1992)
Niblack, C.W., Barber, R., Equitz, W., Flickner, M.D., Taubin, E.H.G.D.P., Yanker, P., Faloutsos, C., Taubin, G.: QBIC project: querying images by content, using color, texture, and shape. In: Storage and Retrieval for Image and Video Databases, San Jose, USA (1993)
Santini, S., Jain, R.: Beyond query by example. In: ACM Multimedia, Bristol, UK (1998)
Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Trans. Image Process. (2000)
Slaney, M., White, W.: Similarity Based on Rating Data. In: 8th Int. Conf. on Music Information Retrieval (ISMIR), Vienna, Austria (2007)
Babas, K., Chalkiadakis, G., Tripolitakis, E.: You Are What You Consume: A Bayesian Method for Personalized Recommendations. In: RecSys 2013, Hong Kong, China (2013)
Jeckmans, A.J.P., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L., Tang, Q.: Privacy in Recommender Systems. Social Media Retrieval, 263–281 (2013)
Polat, H., Du, W.: SVD-based collaborative filtering with privacy. In: ACM Symp. on Applied Computing (SAC), Santa Fe, USA (2005)
McSherry, F., Mironov, I.: Differentially Private Recommender Systems: Building Privacy into the Netflix Prize Contenders. In: ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), Paris, France (2009)
Selke, J., Balke, W.T.: Extracting Features from Ratings: The Role of Factor Models. In: Workshop on Advances in Preference Handling, Lisbon, Portugal (2011)
Khoshneshin, M., Street, W.: Collaborative filtering via euclidean embedding. In: 4th ACM Conf. on Recommender Systems (RecSys), Chicago, Illinois, USA (2010)
Lofi, C., Nieke, C., Balke, W.-T.: Mobile Product Browsing Using Bayesian Retrieval. In: IEEE Conf. Commerce and Enterprise Comp. (CEC), Shanghai, China (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Lofi, C., Nieke, C. (2014). Exploiting Perceptual Similarity: Privacy-Preserving Cooperative Query Personalization. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Springer, Cham. https://doi.org/10.1007/978-3-319-11749-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-11749-2_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11748-5
Online ISBN: 978-3-319-11749-2
eBook Packages: Computer ScienceComputer Science (R0)