Active relevance feedback for difficult queries

Relevance feedback has been demonstrated to be an effective strategy for improving retrieval accuracy. The existing relevance feedback algorithms based on language models and vector space models are not effective in learning from negative feedback documents, which are abundant if the initial query is difficult. The probabilistic retrieval model has the advantage of being able to naturally improve the estimation of both the relevant and non-relevant models by exploiting positive and negative feedback information. The Dirichlet compound multinomial (DCM) distribution, which relies on hierarchical Bayesian modeling techniques, is a more appropriate generative model for the probabilistic retrieval model than the traditional multinomial distribution. We propose a new relevance feedback algorithm, based on a mixture model of the DCM distribution, to effectively utilize the information from both the positive and negative feedback documents by modeling the overlaps between the positive and ne...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Zuobing Xu, Ram Akella
• Keywords: CIKM 2008, Information Management, Negative Feedback, Negative Feedback Documents, Relevance Feedback