Fourth Italian Information Retrieval Workshop IIR 2013

In this paper, we present some ideas about possible directions of a new interpretation of the Okapi BM25 ranking formula. In particular, we have focused on a full bayesian approach for deriving a smoothed formula that takes into account a-priori knowledge on the probability of terms. In fact, most of the efforts in improving the BM25 were done in capturing the language model (frequencies, length, etc.) but missed the fact that the constant equal to 0.5 used as a correction factor can be one of the parameters that can be modelled in a better way. This approach has been tested on a visual data mining tool and the initial results are encouraging.