A Bayesian Approach for Learning Document Type Relevance

Abstract

Retrieval accuracy can be improved by considering which document type should be filtered out and which should be ranked higher in the result list. Hence, document type can be used as a key factor for building a re-ranking retrieval model. We take a simple approach for considering document type in the retrieval process. We adapt the BM25 scoring function to weight term frequency based on the document type and take the Bayesian approach to estimate the appropriate weight for each type. Experimental results show that our approach improves on search precision by as much as 19%.