Resources

  • S. E. Robertson and K. Spärck Jones. 1976. Relevance Weighting of Search Terms. Journal of the American Society for Information Sciences 27(3): 129–146.

  • C. J. van Rijsbergen. 1979. Information Retrieval. 2nd ed. London: Butterworths, chapter 6. [Most details of math] http://www.dcs.gla.ac.uk/Keith/Preface.html

  • N. Fuhr. 1992. Probabilistic Models in Information Retrieval. The Computer Journal, 35(3),243–255. [Easiest read, with BNs]

  • F. Crestani, M. Lalmas, C. J. van Rijsbergen, and I. Campbell. 1998. Is This Document Relevant? ... Probably: A Survey of Probabilistic Models in Information Retrieval. ACM Computing Surveys 30(4): 528–552.

  • http://www.acm.org/pubs/citations/journals/surveys/1998-30-4/p528-crestani/

  • [Adds very little material that isn’t in van Rijsbergen or Fuhr ]

Resources

  • H.R. Turtle and W.B. Croft. 1990. Inference Networks for Document Retrieval. Proc. ACM SIGIR: 1-24.

  • E. Charniak. Bayesian nets without tears. AI Magazine 12(4): 50-63 (1991). http://www.aaai.org/Library/Magazine/Vol12/12-04/vol12-04.html

  • D. Heckerman. 1995. A Tutorial on Learning with Bayesian Networks. Microsoft Technical Report MSR-TR-95-06

  • http://www.research.microsoft.com/~heckerman/

  • N. Fuhr. 2000. Probabilistic Datalog: Implementing Logical Information Retrieval for Advanced Applications. Journal of the American Society for Information Science 51(2): 95–110.

  • R. K. Belew. 2001. Finding Out About: A Cognitive Perspective on Search Engine Technology and the WWW. Cambridge UP 2001.

  • MIR 2.5.4, 2.8