Relevance Feedback with Latent Variables in Riemann spaces
This work addresses relevance feedback for information retrieval systems, presenting incremental improvements with a novel evaluation approach.
The paper tackled the problem of relevance feedback in information retrieval by developing two methods that use a Riemann metric in a semantic query space derived from positive feedback samples, resulting in a new experimental methodology for neutral evaluation and comparison of these methods.
In this paper we develop and evaluate two methods for relevance feedback based on endowing a suitable "semantic query space" with a Riemann metric derived from the probability distribution of the positive samples of the feedback. The first method uses a Gaussian distribution to model the data, while the second uses a more complex Latent Semantic variable model. A mixed (discrete-continuous) version of the Expectation-Maximization algorithm is developed for this model. We motivate the need for the semantic query space by analyzing in some depth three well-known relevance feedback methods, and we develop a new experimental methodology to evaluate these methods and compare their performance in a neutral way, that is, without making assumptions on the system in which they will be embedded.