Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
A framework for selective query expansion
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Regularized estimation of mixture models for robust pseudo-relevance feedback
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Combining fields for query expansion and adaptive query expansion
Information Processing and Management: an International Journal
Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A cluster-based resampling method for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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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.