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Applying Collaborative Filtering to Real-life Corporate Data

  • Miha Grcar
  • Dunja Mladenič
  • Marko Grobelnik
Conference paper
  • 1.6k Downloads
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this paper, we present our experience in applying collaborative filtering to real-life corporate data. The quality of collaborative filtering recommendations is highly dependent on the quality of the data used to identify users’ preferences. To understand the influence that highly sparse server-side collected data has on the accuracy of collaborative filtering, we ran a series of experiments in which we used publicly available datasets and, on the other hand, a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering.

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References

  1. BREESE, J.S., HECKERMAN, D., and KADIE, C. (1998): Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.Google Scholar
  2. CLAYPOOL, M., LE, P., WASEDA, M., and BROWN, D. (2001): Implicit Interest Indicators. In: Proceedings of IUI’01.Google Scholar
  3. DEERWESTER, S., DUMAIS, S.T., and HARSHMAN, R. (1990): Indexing by Latent Semantic Analysis. In: Journal of the Society for Information Science, Vol. 41, No. 6, 391–407.Google Scholar
  4. GOLDBERG, K., ROEDER, T., GUPTA, D., and PERKINS, C. (2001): Eigentaste: A Constant Time Collaborative Filtering Algorithm. In: Information Retrieval, No. 4, 133–151.CrossRefGoogle Scholar
  5. GRCAR, M. (2004): User Profiling: Collaborative Filtering. In: Proceedings of SIKDD 2004 at Multiconference IS 2004, 75–78.Google Scholar
  6. HERLOCKER, J.L., KONSTAN, J.A., TERVEEN, L.G., and RIEDL, J.T. (2004): Evaluating Collaborative Filtering Recommender Systems. In: ACM Transactions on Information Systems, Vol. 22, No. 1, 5–53.CrossRefGoogle Scholar
  7. HOFMANN, T. (1999): Probabilistic Latent Semantic Analysis. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence.Google Scholar
  8. MELVILLE, P., MOONEY, R.J., and NAGARAJAN, R. (2002): Content-boosted Collaborative Filtering for Improved Recommendations. In: Proceedings of the 18th National Conference on Artificial Intelligence, 187–192.Google Scholar
  9. RESNICK, P., IACOVOU, N., SUCHAK, M., BERGSTROM, P., and RIEDL, J. (1994): GroupLens: An Open Architecture for Collaborative Filtering for Netnews. In: Proceedings of CSCW’94, 175–186.Google Scholar
  10. ROSENSTEIN, M. (2000): What is Actually Taking Place on Web Sites: E-Commerce Lessions from Web Server Logs. In: Proceedings of EC’00.Google Scholar

Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • Miha Grcar
    • 1
  • Dunja Mladenič
    • 1
  • Marko Grobelnik
    • 1
  1. 1.Jozef Stefan InstituteLjubljanaSlovenia

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