Abstract
The massive data generated by devices and by people creates an enormous number of possibilities for applications and services. As the combinations of data, services and applications explodes, personalization comes as a meaningful way to filter data and present it in useful ways to users taking into account both the individual features of each user or characteristics of groups of users as well as the desired properties of the selected data, such as serendipity, coverage and diversity. In this chapter, we will start by providing a broad definition of personalization. We will examine different forms of data personalization, ranging from customization to recommendations to personalized search and exploration. We will describe properties of data personalization, such as serendipity and diversity, and we will present representative methods that achieve personalized results of different properties. Then, we will describe how preferences, either at the group or the individual level can be modelled, and we will conclude the chapter with an overview of user preference learning methods.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Agrawal, R., Wimmers, E.L.: A framework for expressing and combining preferences. SIGMOD 29(2), 297–306 (2000)
Armentano, M., Amandi, A.: Goal recognition with variable-order markov models. In: IJCAI, pp. 1635–1640 (2009)
Balabanovic, M., Shoham, Y.: FAB: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content-based information in recommendation. In: AAAI, pp. 714–720 (1998)
Boone, G.: Concept features in re:agent, an intelligent email agent. In: 2nd International Conference on Autonomous Agents (Agents ’98), pp. 195–204 (1998)
Bowen, J.P., Filippini-Fantoni, S.: Personalization and the web from a museum perspective. In: Museums and the Web (2004)
Brajnik, G., Guida, G., Tasso, C.: User modeling in intelligent information retrieval. Inf. Process. Manag. 23(4), 305–320 (1987)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: 14th Conference on Uncertainty in Artificial Intelligence, UAI, pp. 43–52 (1998)
Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)
Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: The Adaptive Web, pp. 3–53 (2007)
Bueno, D., David, A.: METIORE: A Personalized Information Retrieval System. Lecture Notes in Artificial Intelligence, vol. 2109, pp. 168–177. Springer, Heidelberg/New York (2001)
Burke, R.: The wasabi personal shopper: a case-based recommender system. In: 16th National Conference on Artificial Intelligence and the 11th Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, pp. 844–849 (1999)
Burke, R., Hammond, K., Young, B.C.: Knowledge-based navigation of complex information spaces. In: 13th National Conference on Artificial Intelligence, pp. 462–468 (1996)
Charniak, E., Goldman, R.P.: Plan recognition in stories and in life. CoRR abs/1304.1497 (2013)
Chien, Y.H., George, E.: A bayesian model for collaborative filtering. In: Seventh International Workshop Artificial Intelligence and Statistics (1999)
Chomicki, J.: Preference formulas in relational queries. ACM Trans. Database Syst. 28(4), 427–466 (2003)
Crook, P.A., Lemon, O.: Representing uncertainty about complex user goals in statistical dialogue systems. In: SIGDIAL Conference, pp. 209–212 (2010)
Deshpande, M., Karypis, G.: Selective markov models for predicting web page accesses. ACM Trans. Internet Technol. 4(2), 163–184 (2004)
El-Sayed, M., Ruiz, C., Rundensteiner, E.: Fs-miner: efficient and incremental mining of frequent sequence patterns in web logs. In: WIDM (2004)
Foltz, P., Dumais, S.: Personalized information delivery: an analysis of information filtering methods. Commun. ACM 35(12), 51–60 (1992)
Fu, X., Budzik, J., Hammond, K.: Mining navigation history for recommendation. In: IUI (2000)
Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User profiles for personalized information access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. Lecture Notes in Computer Science, vol. 4321, pp. 54–89. Springer, Berlin/Heidelberg (2007)
Getoor, L., Sahami, M.: Using probabilistic relational models for collaborative filtering. In: WebKDD (1999)
Goldberg, D., Nichols, D., Oki, D., Terry, D.: Using collaborative filtering to weave an information tapestry communications of acm. Commun. ACM 35(12), 61–70 (1992)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: ACM SIGIR Conference, pp. 230–237 (1999)
Herrera, M.R., de Moura, E.S., Cristo, M., Silva, T.P.C., da Silva, A.S.: Exploring features for the automatic identification of user goals in web search. Inf. Process. Manag. 46(2), 131–142 (2010)
Hitzeman, J., Mellish, C., Oberlander, J.: Generation of museum web pages: the intelligent labelling explorer. In: Museums and the Web (1997)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–105 (2004)
Jeh, G., Widom, J.: Scaling personalized web search. In: 12th International World Wide Web Conference (2003)
Joachims, T., Freitag, D., Mitchell, T.: Webwatcher: a tour guide for the world wide web. In: Proceedings of the International Joint Conference on Artificial Intelligence (1996)
Jokela, S., Turnpeinen, M., Kurki, T., Savia, E., Sulonen, R.: The role of structured content in a personalised news service. In: 34th Hawaii International Conference on System Sciences, pp. 1–10 (2001)
Kahraman, H.T., Sagiroglu, S., Colak, I.: The development of intuitive knowledge classifier and the modeling of domain dependent data. Knowl. Based Syst. 37(3), 283–295 (2013)
Kautz, H., Selman, B., Shah, M.: Referralweb: combining social networks and collaborative filtering. Commun. ACM 40(3), 63–65 (1997)
Kazienko, P.: Mining indirect association rules for web recommendation. Int. J. Appl. Math. Comput. Sci. 19(1), 165–186 (2009)
Kent, A.: Knowledge-based recommender systems. In: Encyclopedia of Library and Information Systems, vol. 69, pp. 99–111. CRC Press, Boca Raton (2000)
Kießling, W., Kostler, W.: Foundations of preferences in database systems. In: VLDB Conference (2002)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. In: Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (1998)
Koutrika, G., Ioannidis, Y.: Personalization of queries in database systems. In: ICDE, pp. 597–608 (2004)
Koutrika, G., Ioannidis, Y.: Personalized queries under a generalized preference model. In: ICDE Conference (2005)
Krulwich, B., Burkey, C.: Learning user information interests through extraction of semantically significant phrases. In: AAAI Spring Symposium on Machine Learning in Information Access, Stanford (1996)
Lesh, N., Rich, C., Sidner, C.L.: Using plan recognition in human-computer collaboration. In: Proceedings of International Conference on User modeling, pp. 23–32. Springer, New York (1999)
Maragoudakis, M., Thanopoulos, A., Fakotakis, N.: Meteobayes: effective plan recognition in a weather dialogue system. IEEE Intell. Syst. 22(1), 67–77 (2007). doi:10.1109/MIS.2007.14
Middleton, S., Shadbolt, N., Roure, D.D.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)
Minio, M., Tasso, C.: User modeling for information filtering on internet services: exploiting an extended version of the umt shell. In: UM96 Workshop on User Modeling for Information Filtering on the WWW (1996)
Mooney, R., Roy, L.: Content-based book recommending using learning for text categorization. In: 5th ACM Conference on Digital Libraries, pp. 195–204 (2000)
Oberlander, J., O’Donell, M., Mellish, C., Knott, A.: Conversation in the museum: experiments in dynamic hypermedia with the intelligent labeling explorer. In: The New Review of Multimedia and Hypermedia, pp. 11–32 (1998)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Tech. rep., Stanford University Database Group (1998)
Papadimitriou, D., Velegrakis, Y., Koutrika, G., Mylopoulos, J.: Goals in social media, information retrieval and intelligent agents. In: ICDE (2015)
Paterno, F., Mancini, C.: Designing web user interfaces adaptable to different types of use. In: Museums and the Web (1999)
Pazzani, M.J., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)
Pitkow, J., Schutze, H., Cass, T., Cooley, R., TurnBull, D., Edmonds, A., Adar, E., Breuel, T.: Personalized search. Commun. ACM 45(9), 50–55 (2002)
Colace, F., Moscato, V., Quintarelli, E., Rabosio, E., Tanca, L.: Context awareness in pervasive information management. In: Colace, F., De Santo, M., Moscato, V., Picariello, A., Schreiber, F.A., Tanca, L. (eds.) Data Management in Pervasive Systems. Springer, Heidelberg/New York (2015)
Rafter, R., Bradley, K., Smyth, B.: In: Brusilovsky, P., Stock, O., Strapparava, C. (eds.) Adaptive Hypermedia and Adaptive Web-Based Systems. Lecture Notes in Computer Science, vol. 1892, pp. 363–368. Springer, Berlin/Heidelberg (2000)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Conference on Computer Supported Cooperative Work, pp. 75–186 (1994)
Riedl, M.O.: Narrative generation: balancing plot and character. Ph.D. thesis, North Carolina State University (2004)
Rose, D.E., Levinson, D.: Understanding user goals in web search. In: WWW, pp. 13–19. ACM, New York (2004)
Sarwat, M., Bao, J., Chow, C.Y., Levandoski, J., Magdy, A., Mokbel, M.F.: Context awareness in mobile systems. In: Data Management in Pervasive Systems. Springer, Heidelberg (2015)
Schafer, J.B., Konstan, J.A., Riedl, J.: Meta-recommendation systems: user-controlled integration of diverse recommendations. In: 11th International Conference on Information and Knowledge Management (CIKM) (2002)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. In: ACM CHI Conference, pp. 210–217 (1995)
Sieg, A., Mobasher, B., Lytinen, S., Burke, R.: Concept based query enhancement in the ARCH search agent. In: International Conference on Internet Computing, pp. 613–619 (2003)
Sorensen, H., McElligot, M.: Psun: a profiling system for usenet news. In: CKIM 95 Workshop on Intelligent Information Agents (1995)
Stefanidis, K., Koutrika, G., Pitoura, E.: A survey on representation, composition and application of preferences in database systems. ACM Trans. Database Syst. 36(3), 19 (2011)
Tanudjaja, F., Mui, L.: Persona: a contextualized and personalized web search. In: 35th Hawaii International Conference on System Sciences (2002)
VanLehn, K.: Student models. In: Polson, M.C., Richardson, J.J. (eds.) Foundations of Intelligent Tutoring Systems. Laurence Erlbaum, Hillsdale (1988)
Yan, T., Garcia-Molina, H.: Sift: a tool for wide area information dissemination. In: USENIX Technical Conference, pp. 177–186 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Koutrika, G. (2015). Data Personalization. In: Colace, F., De Santo, M., Moscato, V., Picariello, A., Schreiber, F., Tanca, L. (eds) Data Management in Pervasive Systems. Data-Centric Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20062-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-20062-0_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20061-3
Online ISBN: 978-3-319-20062-0
eBook Packages: Computer ScienceComputer Science (R0)