A bayesian logistic regression model for active relevance feedback

Relevance feedback, which traditionally uses the terms in the relevant documents to enrich the user's initial query, is an effective method for improving retrieval performance. The traditional relevance feedback algorithms lead to overfitting because of the limited amount of training data and large term space. This paper introduces an online Bayesian logistic regression algorithm to incorporate relevance feedback information. The new approach addresses the overfitting problem by projecting the original feature space onto a more compact set which retains the necessary information. The new set of features consist of the original retrieval score, the distance to the relevant documents and the distance to non-relevant documents. To reduce the human evaluation effort in ascertaining relevance, we introduce a new active learning algorithm based on variance reduction to actively select documents for user evaluation. The new active learning algorithm aims to select feedback documents to ...

• Type: Journal
• Year: 2008
• Where: SIGIR
• Authors: Zuobing Xu, Ram Akella
• Keywords: Algorithms, Documents, Information Technology, Relevance Feedback, SIGIR 2008