A unified maximum likelihood approach to document retrieval

Empirical work shows significant benefits from using relevance feedback dat a to improve information retrieval (IR) performance. Still, one fundamental difficulty has limited the ability to fully exploit this valuable data. Th e problem is that it is not clear whether the relevance feedback data shoul d be used to train the system about what the users really mean, or about wh at the documents really mean. In this paper, we resolve the question using a maximum likelihood framework. We show how all the available data can be u sed to simultaneously estimate both documents and queries in proportions th at are optimal in a maximum likelihood sense. The resulting algorithm is di rectly applicable to many approaches to IR, and the unified framework can h elp explain previously reported results as well as guide the search for new methods that utilize feedback data in IR.