Contextual Recommendation

  • Sarabjot Singh Anand
  • Bamshad Mobasher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4737)


The role of context in our daily interaction with our environment has been studied in psychology, linguistics, artificial intelligence, information retrieval, and more recently, in pervasive/ubiquitous computing. However, context has been largely ignored in research into recommender systems specifically and personalization in general. In this paper we describe how context can be brought to bear on recommender systems. As a means for achieving this, we propose a fundamental shift in terms of how we model a user within a recommendation system: inspired by models of human memory developed in psychology, we distinguish between a user’s short term and long term memories, define a recommendation process that uses these two memories, using context-based retrieval cues to retrieve relevant preference information from long term memory and use it in conjunction with the information stored in short term memory for generating recommendations. We also describe implementations of recommender systems and personalization solutions based on this framework and show how this results in an increase in recommendation quality.


Short Term Memory Active User Long Term Memory Recommender System Preference Model 
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.
    Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Modelling and User-Adapted Interaction 10, 147–180 (2000)CrossRefGoogle Scholar
  2. 2.
    Suchman, L.: Plans and Situtated Actions. Cambridge University Press, Cambridge (1987)Google Scholar
  3. 3.
    Chi, E.H.: Transient user profiles. In: Proceedings of the Workshop on User Profiling, pp. 521–523 (2004)Google Scholar
  4. 4.
    Smith, S.M.: Remembering in and out of context. Journal of Experimental Psychology: Human Learning and Memory 5, 460–471 (1979)CrossRefGoogle Scholar
  5. 5.
    Bartlett, J.C., Santrock, J.: Affect-depedent episodic memory in young children. Child Development 5, 513–518 (1979)CrossRefGoogle Scholar
  6. 6.
    Leech, G.: Semantics: The Study of Meaning, 2nd edn. Penguin (1981)Google Scholar
  7. 7.
    Schilit, B., Theimer, M.: Disseminating active map information to mobile hosts. IEEE Network 8, 22–32 (1994)CrossRefGoogle Scholar
  8. 8.
    Dourish, P.: What do we talk about when we talk about context. Personal and Ubiquitous Computing 8(1), 19–30 (2004)CrossRefGoogle Scholar
  9. 9.
    Mayrhofer, R., Radi, H., Ferscha, A.: Recognizing and predicting context by learning from user behavior. Radiomatics: Journal of Communication Engineering, special issue on Advances in Mobile Multimedia 1(1), 30–42 (2004)Google Scholar
  10. 10.
    Dourish, P., Edwards, W.K., LaMarca, A., Lamping, J., Petersen, K., Salisbury, M., Terry, D.B., Thornton, J.: Extending document management systems with user-specific active properties. ACM Transactions on Information Systems 18(2), 140–170 (2000)CrossRefGoogle Scholar
  11. 11.
    Brumitt, B., Meyers, K.J., Kern, A., Shafer, S.: Easyliving: Technologies for intelligent environments. In: Handheld and Ubiquitous Computing (September 2000)Google Scholar
  12. 12.
    Lieberman, H., Selker, T.: Out of context: Computer systems that adapt to, and learn from, context. IBM Systems Journal 39(3 & 4) (2000)Google Scholar
  13. 13.
    Dey, A.K.: Understanding and using context. Personal and Ubiquitous Computing 5(1), 4–7 (2001)CrossRefGoogle Scholar
  14. 14.
    Sieg, A., Mobasher, B., Burke, R.: Inferring user’s information context: Integrating user profiles and concept hierarchies. In: Proceedings of the 2004 Meeting of the International Federation of Classification Societies (2004)Google Scholar
  15. 15.
    Kraft, R., Maghoul, F., Chang, C.C.: Y!q: Context search at the point of inspiration. In: Proceedings of the ACM Conference on Information and Knowledge Management, pp. 816–823. ACM Press, New York (2005)CrossRefGoogle Scholar
  16. 16.
    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems 1(1) (1999)Google Scholar
  17. 17.
    Jin, X., Zhou, Y., Mobasher, B.: Task-oriented web user modeling for recommendation. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, Springer, Heidelberg (2005)Google Scholar
  18. 18.
    Smyth, P.: Clustering using monte carlo cross-validation. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 126–133 (1996)Google Scholar
  19. 19.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of -the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)CrossRefGoogle Scholar
  20. 20.
    Anand, S.S., Mobasher, B.: Intelligent techniques in web personalization. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 1–37. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  21. 21.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modelling and User Adapted Interaction 12(4), 331–370 (2002)zbMATHCrossRefGoogle Scholar
  22. 22.
    Atkinson, R.C., Shiffrin, R.M.: Human memory: A proposed system and its control processes. Psychology of Learning and Motivation 2, 89–195 (1968)CrossRefGoogle Scholar
  23. 23.
    Raaijmakers, J.G.W., Shiffrin, R.M.: Sam: A theory of probabilistic search of associative memory. The Psychology of Learning and Motivation 14, 207–262 (1980)CrossRefGoogle Scholar
  24. 24.
    Anand, S.S., Kearney, P., Shapcott, M.: Generating semantically enriched user profiles for web personalization. ACM Transactions on Internet Technologies 7(2) (to appear, 2007)Google Scholar
  25. 25.
    Dai, H., Mobasher, B.: A road map to more effective web personalization: Integrating domain knowledge with web usage mining. In: Proceedings of the International Conference on Internet Computing, pp. 58–64 (2003)Google Scholar
  26. 26.
    Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society of Industrial and Applied Mathematics 5(1), 32–38 (1957)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Kullback, S., Leibler, R.A.: On information and sufficiency. Annals of Mathematical Statistics 22(1), 79–86 (1951)zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Transactions on Information Systems 23, 103–145 (2005)CrossRefGoogle Scholar
  29. 29.
    Berendt, B., Teltzrow, M.: Addressing users’ privacy concerns for improving personalization quality: Towards an integration of user studies and algorithm evaluation. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  30. 30.
    Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: Computer Supported Cooperative Work, pp. 345–354 (1998)Google Scholar
  31. 31.
    Hayes, P.C.C.: Context boosting collabirative recommendations. Knowledge Based Systems 17, 131–138 (2004)CrossRefGoogle Scholar
  32. 32.
    Forbus, K.D., Gentner, D., Law, K.: Mac/fac: A model of similarity-based retrieval. Cognitive Science 19(2), 141–205 (1994)CrossRefGoogle Scholar
  33. 33.
    Fabio Gasparetti, A.M.: User profile generation based on a memory retrieval theory. In: WPRSIUI 2005. Proc. 1st International Workshop on Web Personalization, Recommender Systems and Intelligent User Interfaces (2005)Google Scholar
  34. 34.
    Chi, E.H., Pirolli, P., Pitkow, J.E.: The scent of a site: a system for analyzing and predicting information scent, usage, and usability of a web site. In: Proceedings of the ACM Conference on Human Factors in COmputing Systems, pp. 161–168. ACM Press, New York (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sarabjot Singh Anand
    • 1
  • Bamshad Mobasher
    • 2
  1. 1.Department of Computer Science, University of Warwick, Coventry CV4 7ALUK
  2. 2.Center for Web Intelligence, School of Computer Science, Telecommunications and Information Systems, DePaul University, Chicago, IllinoisUSA

Personalised recommendations