Relevance Feedback Fusion via Query Expansion

Relevance Feedback (RF) is an important technique to improve information retrieval and has emerged as one of the hottest topics for both the industry and academic researchers. The performance of RF depends on feedback information. As the volume of feedback information gradually increases, Explicit Relevance Feedback (ERF) is attractive. In this paper, we focus on the ERF in which Feedback information is given. With regards to the lack of information in ERF, we apply Pseudo Relevance Feedback (PRF) to enhance retrieval effectiveness. However, the instability of PRF can result in a negative impact on retrieval performance. We use feedback information to define features and propose a classification model to predict which query can benefit from PRF. Then, we form relevance feedback fusion by the prediction of PRF performance. This method is designed to exploit the strengths of PRF and ERF while avoiding some weaknesses of these approaches. Experiment results show that our approach is feasible and effective.