A similarity-based approach to relevance learning

In several information retrieval (IR) systems there is a possibility for user feedback. Many machine learning methods have been proposed that learn from the feedback information in a longterm fashion. In this paper, we present an approach that builds on user feedback across multiple queries in order to improve the retrieval quality of novel queries. This allows users of an IR system to retrieve relevant documents at a reduced effort. Two algorithms for long-term learning across multiple queries in the scope of the retrieval system Latent Semantic Indexing have been implemented in a system, REGRESSOR, in order to test these ideas. The algorithms are based on k-nearest-neighbor searching and back propagation neural networks. Training examples are query vectors, and by using Latent Semantic Indexing, the examples are reduced to a fixed and manageable size. In order to evaluate the methods, we performed a set of experiments where we compared the performance of Latent Semantic Indexing and REGRESSOR. The results demonstrate that REGRESSOR automatically improves on the performance of Latent Semantic Indexing by utilizing the feedback information from past queries.