generative model-based metasearch for data fusion in information retrieval
"Data fusion" refers to the problem in information retrieval (IR) where several lists of documents ranked against a query are to be merged into a single ranked list for presentation to a user. Data fusion is also known as "metasearch." In a digital library setting data fusion may support operations such as federated search based on multiple repository representations. This paper presents a novel approach to the fusion problem: generative model-based Metasearch (GeM). We suggest viewing the appearance of documents in a return set as the outcome of a probabilistic process; some documents are likely to occur in the model, while others are unlikely. Using Bayesian parameter estimation to fit a multinomial distribution based on the return sets to be merged, GeM achieves a final ranking by listing documents in decreasing probability of generation under the induced model. We also introduce what we call "the impatient reader" approach to normalizing document ranks in service to the fusion operation. We report results from several experiments on TREC data suggesting that GeM, informed with impatient reader document scores, operates at state-of-the-art levels of effectiveness.
metasearch data fusion information retrieval digital libraries probabilistic models generative models