Comparing neural and probabilistic relevance feedback in an interactive information retrieval system

This paper presents the results of an experimental investigation into the use of neural networks for implementing relevance feedback in an interactive information retrieval system. The most advanced relevance feedback technique used in operative interactive information retrieval systems, probabilistic relevance feedback, is compared with a neural networks based technique. The latest uses the learning and generalisation capabilities of a 3-layer feedforward neural network with the backpropagation learning procedure to distinguish between relevant and non-relevant documents. A comparative evaluation between the two techniques is performed using an advanced information retrieval system, a neural network simulator, and an IR test document collection. The results are reported and explained from an information retrieval point of view.<>