A probabilistic theory of indexing and similarity measure based on cited and citing documents

A new model of viewing a document based on the citingcited relationship between documents is introduced. Using Bayes' decision theory, it is shown how a source document may be indexed and weighted by its set of relevant cited or citing document features, corresponding to a one pass relevance feedback Model 1 (probabilistic indexing) or Model 2 (probabilistic retrieval) system of [8]. Once every document in a collection has been so indexed, various forms of similarity measures based on probability of topical relevance between documents are derivable, including asymmetric, symmetric, and the relationship with Model 3 of [8]. Applications to retrieval and document clustering are also discussed.