Performance Prediction in Recommender Systems

  • Alejandro Bellogín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6787)


Research on Recommender Systems has barely explored the issue of adapting a recommendation strategy to the user’s information available at a certain time. In this thesis, we introduce a component that allows building dynamic recommendation strategies, by reformulating the performance prediction problem in the area of Information Retrieval to that of recommender systems. More specifically, we investigate a number of adaptations of the query clarity predictor in order to infer the ambiguity in user and item profiles. The properties of each predictor are empirically studied by, first, checking the correlation of the predictor output with a performance measure, and second, by incorporating a performance predictor into a recommender system to produce a dynamic strategy. Depending on how the predictor is integrated with the system, we explore two different applications: dynamic user neighbour weighting and hybrid recommendation. The performance of such dynamic strategies is examined and compared with that of static ones.


recommender systems performance prediction query clarity personalisation user modelling 


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 Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Bellogín, A., Castells, P.: A Performance Prediction Approach to Enhance Collaborative Filtering Performance. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 382–393. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: ACM SIGIR 1999 Workshop on Recommender Systems: Algorithms and Applications (1999)Google Scholar
  4. 4.
    Cronen-Townsend, S., Zhou, Y., Croft, B.W.: Predicting Query Performance. In: 25th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), pp. 299–306. ACM Press, New York (2002)Google Scholar
  5. 5.
    Hauff, C.: Predicting the Effectiveness of Queries and Retrieval Systems. PhD thesis, University of Twente, Enschede (2010)Google Scholar
  6. 6.
    Lathia, N., Hailes, S., Capra, L.: Temporal Collaborative Filtering with Adaptive Neighbourhoods. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009), pp. 796–797. ACM Press, New York (2009)Google Scholar
  7. 7.
    O’Donovan, J., Smyth, B.: Trust in Recommender Systems. In: 10th International Conference on Intelligent User Interfaces (IUI 2005), pp. 167–174. ACM Press, New York (2005)Google Scholar
  8. 8.
    Wang, J., de Vries, A.P., Reinders, M.J.T.: Unified relevance models for rating prediction in collaborative filtering. ACM Trans. Inf. Syst. 26(3), 1–42 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alejandro Bellogín
    • 1
  1. 1.Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

Personalised recommendations