An interactive information-retrieval method based on active learning

Abstract

Comprehending user demands through several human-computer interactions can effectively increase informationretrieval accuracy. Mainstream active learning algorithms use uncertainty sampling strategy. However, such algorithms cannot produce satisfactory results under few interactions. To improve interactive information-retrieval efficiency and accuracy, an sampling strategy based on the error-correcting capacity of samples was proposed for active learning. This strategy evaluated the expected value of unlabeled samples by calculating their potential error-correcting capacity associated with the classifier. Based on this sampling strategy, a fast interactive information-retrieval scheme adopting reinforcement learning and low-complexity classifier was designed in this study. The effects of three sampling strategies (random sampling, uncertainty sampling, and the proposed sampling strategy based on error-correcting capacity) on information-retrieval accuracy were examined using an experiment through a text set of Reuters-21578. Experimental results demonstrated that the proposed sampling strategy achieved higher retrieval accuracy and stability than random and uncertainty samplings. The retrieval accuracy of the proposed scheme was approximately 1.6% higher than that of the sampling algorithm based on uncertainty strategy. The proposed scheme can be used for real-time information retrieval because of its low computational complexity. The production of this study can improve the accuracy and latency of interactive information-retrieval services.