A statistical view of binned retrieval models

Many traditional information retrieval models, such as BM25 and language modeling, give good retrieval effectiveness, but can be difficult to implement efficiently. Recently, document-centric impact models were developed in order to overcome some of these efficiency issues. However, such models have a number of problems, including poor effectiveness, and heuristic term weighting schemes. In this work, we present a statistical view of document-centric impact models. We describe how such models can be treated statistically and propose a supervised parameter estimation technique. We analyze various theoretical and practical aspects of the model and show that weights estimated using our new estimation technique are significantly better than the integer-based weights used in previous studies.