Modern text collections often contain large documents which span several subject areas. Such documents are problematic for relevance feedback since inappropriate terms can easily be chosen. This study explores the highly eeective approach of feeding back passages of large documents. A less-expensive method which discards long documents is also reviewed and found to be eeective if there are enou...

Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two-class relevance feedback: relevant and irrelevant classes. While simple computationally, two-class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. In this paper we p...

Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user’s judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the firs...

We present results of a study comparing two different interactive information retrieval systems: one which supports positive relevance feedback as a termsuggestion device; the other which supports both positive and negative relevance feedback in this same context. The purpose of the study was to investigate the effectiveness and usability of a specific implementation of negative relevance feedb...

In this paper we present five user experiments on incorporating behavioural information into the relevance feedback process. In particular we concentrate on ranking terms for query expansion and selecting new terms to add to the user’s query. Our experiments are an attempt to widen the evidence used for relevance feedback from simply the relevant documents to include information on how users ar...

In this paper, we present five user experiments on incorporating behavioral information into the relevance feedback process. In particular, we concentrate on ranking terms for query expansion and selecting new terms to add to the user’s query. Our experiments are an attempt to widen the evidence used for relevance feedback from simply the relevant documents to include information on how users a...

We describe the application of probabilistic indexing and retrieval methods to the TREC material. For document indexing, we apply a description-oriented approach which uses relevance feedback information from previous queries run on the same collection. This method is also very exible w.r.t. the underlying document representation. In our experiments, we consider single words and phrases and use...