Add to ORKGThis study outlines an adaptive method which constructs improved query vectors based on the user preference judgments on sample document pairs. In particular, the user states that some documents are preferred to other documents and the system is then expected to rank the preferred documents ahead of the others. In the adaptive system, all needed parameter values are provided within the model, and a solution query vector is constructed under well defined conditions. Certain relationships between the new adaptive and the conventional relevance feedback systems are discussed and evaluation data are provided to demonstrate the effectiveness of the system
Pseudo-relevance feedback finds useful expansion terms from a set of top-ranked documents. It is oft...
Given the difficulty of setting up large-scale experiments with real users, the comparison of conten...
Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By ...
Relevance feedback is the retrieval task where the system is given not only an information need, but...
Relevance feedback is an effective approach to improve re-trieval quality over the initial query. Ty...
Abstract. Relevance feedback algorithm is proposed to be an effective way to improve the precision o...
Automatic relevance feedback, or query expansion, is a common technique in information retrieval sys...
In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-...
Relevance Feedback has proven very effective for improv-ing retrieval accuracy. A difficult yet impo...
Traditional relevance feedback technique could help improve retrieval performance. It usually utiliz...
Users of online search engines often find it difficult to express their need for information in the ...
In this paper we present five user experiments on incorporating behavioural information into the rel...
The relevance feedback process uses information derived from an initially retrieved set of document...
Relevance Feedback is a technique that helps an Information Retrieval system modify a query in respo...
Information Retrieval is the science of searching for information or documents based on information...
Pseudo-relevance feedback finds useful expansion terms from a set of top-ranked documents. It is oft...
Given the difficulty of setting up large-scale experiments with real users, the comparison of conten...
Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By ...
Relevance feedback is the retrieval task where the system is given not only an information need, but...
Relevance feedback is an effective approach to improve re-trieval quality over the initial query. Ty...
Abstract. Relevance feedback algorithm is proposed to be an effective way to improve the precision o...
Automatic relevance feedback, or query expansion, is a common technique in information retrieval sys...
In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-...
Relevance Feedback has proven very effective for improv-ing retrieval accuracy. A difficult yet impo...
Traditional relevance feedback technique could help improve retrieval performance. It usually utiliz...
Users of online search engines often find it difficult to express their need for information in the ...
In this paper we present five user experiments on incorporating behavioural information into the rel...
The relevance feedback process uses information derived from an initially retrieved set of document...
Relevance Feedback is a technique that helps an Information Retrieval system modify a query in respo...
Information Retrieval is the science of searching for information or documents based on information...
Pseudo-relevance feedback finds useful expansion terms from a set of top-ranked documents. It is oft...
Given the difficulty of setting up large-scale experiments with real users, the comparison of conten...
Various relevance feedback techniques have been applied in Content-Based Image Retrieval (CBIR). By ...