Probabilistic Information Retrieval Probabilistic relevance feedback Rather than reweighting in a vector space If user has told us some relevant and some irrelevant documents then we can proceed to build a probabilistic classifier, such as a Naive bayes model P(tkIR)=Drk/D P(tkINr) =Dnrk I /Dnr tk is a term; D is the set of known relevant documents Drk is the subset that contain ti D. is the set of known irrelevant documents D is the subset that contain tProbabilistic Information Retrieval 3 Probabilistic relevance feedback ▪ Rather than reweighting in a vector space… ▪ If user has told us some relevant and some irrelevant documents, then we can proceed to build a probabilistic classifier, such as a Naive Bayes model: ▪ P(tk|R) = |Drk| / |Dr| ▪ P(tk|NR) = |Dnrk| / |Dnr| ▪ tk is a term; Dr is the set of known relevant documents; Drk is the subset that contain tk ; Dnr is the set of known irrelevant documents; Dnrk is the subset that contain tk