Finding relevant information in a hyperspace has been a much studied problem for many years. With the emergence of so called Web 2.0 technologies we have seen the use of social sy...
Rosta Farzan, Maurice Coyle, Jill Freyne, Peter Br...
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
This article addresses a question regarding relevant information in a social media such as a wiki that can contain huge amount of text, written in slang or in natural language, wi...
Carlos Miguel Tobar, Alessandro Santos Germer, Jua...
Learning-enhanced relevance feedback is one of the most promising and active research directions in recent year's content-based image retrieval. However, the existing approac...
We apply a well-known Bayesian probabilistic model to textual information retrieval: the classification of documents based on their relevance to a query. This model was previously...