Preference-Based Query Personalization

  • Georgia KoutrikaEmail author
  • Evaggelia Pitoura
  • Kostas Stefanidis
Part of the Intelligent Systems Reference Library book series (ISRL, volume 36)


In the context of database queries, computational methods for handling preferences can be broadly divided into two categories. Query personalization methods consider that user preferences are provided as a user profile separately from the query and dynamically determine how this profile will affect the query results. On the other hand, preferential query answering methods consider that preferences are explicitly expressed within queries. The focus of this chapter is on query personalization methods. We will first describe how preferences can be represented and stored in user profiles. Then, we will discuss how preferences are selected from a user profile and applied to a query.


Query Processing User Preference Ranking Function Preference Score Aggregate Score 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Rantzau, R., Terzi, E.: Context-Sensitive Ranking. In: SIGMOD, pp. 383–394 (2006)Google Scholar
  3. 3.
    Agrawal, R., Wimmers, E.L.: A Framework for Expressing and Combining Preferences. In: SIGMOD, pp. 297–306 (2000)Google Scholar
  4. 4.
    Amer-Yahia, S., Roy, S.B., Chawla, A., Das, G., Yu, C.: Group Recommendation: Semantics and Efficiency. PVLDB 2(1), 754–765 (2009)Google Scholar
  5. 5.
    Benjelloun, O., Sarma, A.D., Halevy, A.Y., Widom, J.: ULDBs: Databases with Uncertainty and Lineage. In: VLDB, pp. 953–964 (2006)Google Scholar
  6. 6.
    Borda, J.C.: Mémoire sur les Élections au Scrutin. Histoire de l’Académie Royale des Sciences (1781)Google Scholar
  7. 7.
    Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements. J. Artif. Intell. Res. 21, 135–191 (2004)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Brown, P.J., Bovey, J.D., Chen, X.: Context-Aware Applications: From the Laboratory to the Marketplace. IEEE Personal Communications 4(5), 58–64 (1997)CrossRefGoogle Scholar
  9. 9.
    van Bunningen, A.H., Feng, L., Apers, P.M.G.: A Context-Aware Preference Model for Database Querying in an Ambient Intelligent Environment. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 33–43. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Burges, C.J.C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.N.: Learning to Rank using Gradient Descent. In: ICML, pp. 89–96 (2005)Google Scholar
  11. 11.
    Chen, G., Kotz, D.: A Survey of Context-Aware Mobile Computing Research. Tech. Rep. TR2000-381, Dartmouth College, Computer Science (2000),
  12. 12.
    Cherniack, M., Galvez, E.F., Franklin, M.J., Zdonik, S.B.: Profile-Driven Cache Management. In: ICDE, pp. 645–656 (2003)Google Scholar
  13. 13.
    Chomicki, J.: Querying with Intrinsic Preferences. In: Jensen, C.S., Jeffery, K., Pokorný, J., Saltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 34–51. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Ciaccia, P.: Querying databases with incomplete CP-nets. In: M-Pref (2007)Google Scholar
  15. 15.
    Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to Order Things. J. Artif. Intell. Res. (JAIR) 10, 243–270 (1999)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Condorcet, J.A.N.: Éssai sur l’ application de l’ analyse á la Probabilité des Décisions Rendues á la Pluralité des Voix. Kessinger Publishing (1785)Google Scholar
  17. 17.
    Dalvi, N.N., Suciu, D.: Efficient Query Evaluation on Probabilistic Databases. VLDB J. 16(4), 523–544 (2007)CrossRefGoogle Scholar
  18. 18.
    Das, G., Hristidis, V., Kapoor, N., Sudarshan, S.: Ordering the Attributes of Query Results. In: SIGMOD, pp. 395–406 (2006)Google Scholar
  19. 19.
    Dey, A.K.: Understanding and Using Context. Personal Ubiquitous Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  20. 20.
    Drosou, M., Stefanidis, K., Pitoura, E.: Preference-aware Publish/Subscribe Delivery with Diversity. In: DEBS, pp. 1–12 (2009)Google Scholar
  21. 21.
    Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank Aggregation Methods for the Web. In: WWW10 (2001)Google Scholar
  22. 22.
    Endres, M., Kießling, W.: Transformation of TCP-net Queries into Preference Database Queries. In: M-Pref (2006)Google Scholar
  23. 23.
    Fagin, R.: Combining Fuzzy Information from Multiple Systems. In: PODS, pp. 216–226 (1996)Google Scholar
  24. 24.
    Fagin, R.: Fuzzy Queries in Multimedia Database Systems. In: PODS, pp. 1–10 (1998)Google Scholar
  25. 25.
    Fagin, R., Lotem, A., Naor, M.: Optimal Aggregation Algorithms for Middleware. In: PODS (2001)Google Scholar
  26. 26.
    Fishburn, P.C.: Preference Structures and Their Numerical Representations. Theoretical Computer Science 217(2), 359–383 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Fontoura, M., Josifovski, V., Kumar, R., Olston, C., Tomkins, A., Vassilvitskii, S.: Relaxation in Text Search using Taxonomies. PVLDB 1(1), 672–683 (2008)Google Scholar
  28. 28.
    Georgiadis, P., Kapantaidakis, I., Christophides, V., Nguer, E.M., Spyratos, N.: Efficient Rewriting Algorithms for Preference Queries. In: ICDE, pp. 1101–1110 (2008)Google Scholar
  29. 29.
    Güntzer, U., Balke, W.T., Kießling, W.: Optimizing Multi-Feature Queries for Image Databases. In: VLDB, pp. 419–428 (2000)Google Scholar
  30. 30.
    Holland, S., Ester, M., Kießling, W.: Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications. In: Lavrac, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 204–216. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  31. 31.
    Holland, S., Kießling, W.: Situated Preferences and Preference Repositories for Personalized Database Applications. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, T.-W. (eds.) ER 2004. LNCS, vol. 3288, pp. 511–523. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  32. 32.
    Joachims, T.: Optimizing Search Engines using Clickthrough Data. In: KDD, pp. 133–142 (2002)Google Scholar
  33. 33.
    Kießling, W.: Foundations of Preferences in Database Systems. In: VLDB, pp. 311–322 (2002)Google Scholar
  34. 34.
    Kießling, W., Fischer, S., Döring, S.: COSIMAB2B - Sales Automation for E-Procurement. In: CEC, pp. 59–68 (2004)Google Scholar
  35. 35.
    Koutrika, G., Ioannidis, Y.: Constrained Optimalities in Query Personalization. In: SIGMOD, pp. 73–84 (2005)Google Scholar
  36. 36.
    Koutrika, G., Ioannidis, Y.: Personalized Queries under a Generalized Preference Model. In: ICDE, pp. 841–852 (2005)Google Scholar
  37. 37.
    Koutrika, G., Ioannidis, Y.: Personalizing Queries based on Networks of Composite Preferences. ACM Trans. Database Syst. 35(2) (2010)Google Scholar
  38. 38.
    Koutrika, G., Ioannidis, Y.E.: Personalization of Queries in Database Systems. In: ICDE, pp. 597–608 (2004)Google Scholar
  39. 39.
    Lichtenstein, S., Slovic, P.: The Construction of Preference. Cambridge University Press, New York (2006)CrossRefGoogle Scholar
  40. 40.
    Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers. User Modeling and User-Adapted Interaction 14(1), 37–85 (2004)CrossRefGoogle Scholar
  41. 41.
    Miah, M., Das, G., Hristidis, V., Mannila, H.: Standing Out in a Crowd: Selecting Attributes for Maximum Visibility. In: ICDE, pp. 356–365 (2008)Google Scholar
  42. 42.
    Miele, A., Quintarelli, E., Tanca, L.: A Methodology for Preference-based Personalization of Contextual Data. In: EDBT, pp. 287–298 (2009)Google Scholar
  43. 43.
    Miller, G.A.: WordNet: a Lexical Database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  44. 44.
    Mindolin, D., Chomicki, J.: Hierarchical CP-networks. In: M-Pref (2007)Google Scholar
  45. 45.
    Nepal, S., Ramakrishna, M.V.: Query Processing Issues in Image (Multimedia) Databases. In: ICDE, pp. 22–29 (1999)Google Scholar
  46. 46.
    Qu, H., Labrinidis, A.: Preference-Aware Query and Update Scheduling in Web-databases. In: ICDE, pp. 356–365 (2007)Google Scholar
  47. 47.
    Scherer, K.: What are Emotions? And how can they be Measured? Social Science Information 44, 695–729 (2005)CrossRefGoogle Scholar
  48. 48.
    Schmidt, A., Aidoo, K.A., Takaluoma, A., Tuomela, U., Van Laerhoven, K., Van de Velde, W.: Advanced Interaction in Context. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 89–101. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  49. 49.
    Soliman, M.A., Ilyas, I.F., Chang, K.C.C.: Top-k query processing in uncertain databases. In: ICDE, pp. 896–905 (2007)Google Scholar
  50. 50.
    Stefanidis, K., Drosou, M., Pitoura, E.: PerK: Personalized Keyword Search in Relational Databases through Preferences. In: EDBT, pp. 585–596 (2010)Google Scholar
  51. 51.
    Stefanidis, K., Pitoura, E., Vassiliadis, P.: Modeling and Storing Context-Aware Preferences. In: Manolopoulos, Y., Pokorný, J., Sellis, T.K. (eds.) ADBIS 2006. LNCS, vol. 4152, pp. 124–140. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  52. 52.
    Stefanidis, K., Pitoura, E., Vassiliadis, P.: Adding Context to Preferences. In: ICDE, pp. 846–855 (2007)Google Scholar
  53. 53.
    Stefanidis, K., Pitoura, E., Vassiliadis, P.: On Relaxing Contextual Preference Queries. In: MDM, pp. 289–293 (2007)Google Scholar
  54. 54.
    Taylor, A.: Mathematics and Politics: Strategy, Voting, Power and Proof. Springer, New York (1995)zbMATHGoogle Scholar
  55. 55.
    Vassiliadis, P., Skiadopoulos, S.: Modelling and Optimisation Issues for Multidimensional Databases. In: Wangler, B., Bergman, L.D. (eds.) CAiSE 2000. LNCS, vol. 1789, pp. 482–497. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  56. 56.
    Zha, H., Zheng, Z., Fu, H., Sun, G.: Incorporating Query Difference for Learning Retrieval Functions in World Wide Web Search. In: CIKM, pp. 307–316 (2006)Google Scholar
  57. 57.
    Zhai, C., Lafferty, J.D.: A Risk Minimization Framework for Information Retrieval. Information Processing and Management 42(1), 31–55 (2006)zbMATHCrossRefGoogle Scholar
  58. 58.
    Zhang, X., Chomicki, J.: Semantics and Evaluation of Top-k Queries in Probabilistic Databases. Distributed and Parallel Databases 26(1), 67–126 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Georgia Koutrika
    • 1
    Email author
  • Evaggelia Pitoura
    • 2
  • Kostas Stefanidis
    • 3
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.University of IoanninaIoanninaGreece
  3. 3.Norwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations