Add to ORKGUser relevance feedback is usually utilized by Web systems to interpret user information needs and retrieval effective results for users. However, how to discover useful knowledge in user relevance feedback and how to wisely use the discovery knowledge are two critical problems. In TREC 2009, we participated in the Relevance Feedback Track and proposed a model consisting of two innovative stages: one for subject-based query expansion for obtaining pseudo-relevance feedback; one for relevance feature discovery to find useful patterns and terms in relevance feedback for ranking documents. In this document, the detailed descriptions are given, as well as the related discussions
In this paper, we present an alternative approach to the problem of contextual relevance feedback in...
This data archive accompanies our work, in which we analyze a pseudo-relevance retrieval method that...
This paper details our experiments carried out at TREC 2008 Relevance Feedback Track. We focused on ...
User relevance feedback is usually utilized by Web systems to interpret user information needs and r...
In this paper we present five user experiments on incorporating behavioural information into the rel...
Relevance feedback is the retrieval task where the system is given not only an information need, but...
Users of online web engines frequently think that it�s hard to express their requirement for informa...
In recall-oriented search tasks retrieval systems are privy to a greater amount of user feedback. In...
This document contains a description of experiments for the 2008 Relevance Feedback track. We experi...
This thesis is a study for automatic discovery of text features for describing user information need...
It is a big challenge to guarantee the quality of discovered relevance features in text documents fo...
Term-based approaches can extract many features in text documents, but most include noise. Many popu...
It is a big challenge to guarantee the quality of discovered relevance features in text documents fo...
In the face of the information explosion fuelled by the phenomenal growth of the Web, researchers ar...
© 2018 Elsevier Ltd Pseudo-relevance feedback (PRF) has evident potential for enriching the represen...
In this paper, we present an alternative approach to the problem of contextual relevance feedback in...
This data archive accompanies our work, in which we analyze a pseudo-relevance retrieval method that...
This paper details our experiments carried out at TREC 2008 Relevance Feedback Track. We focused on ...
User relevance feedback is usually utilized by Web systems to interpret user information needs and r...
In this paper we present five user experiments on incorporating behavioural information into the rel...
Relevance feedback is the retrieval task where the system is given not only an information need, but...
Users of online web engines frequently think that it�s hard to express their requirement for informa...
In recall-oriented search tasks retrieval systems are privy to a greater amount of user feedback. In...
This document contains a description of experiments for the 2008 Relevance Feedback track. We experi...
This thesis is a study for automatic discovery of text features for describing user information need...
It is a big challenge to guarantee the quality of discovered relevance features in text documents fo...
Term-based approaches can extract many features in text documents, but most include noise. Many popu...
It is a big challenge to guarantee the quality of discovered relevance features in text documents fo...
In the face of the information explosion fuelled by the phenomenal growth of the Web, researchers ar...
© 2018 Elsevier Ltd Pseudo-relevance feedback (PRF) has evident potential for enriching the represen...
In this paper, we present an alternative approach to the problem of contextual relevance feedback in...
This data archive accompanies our work, in which we analyze a pseudo-relevance retrieval method that...
This paper details our experiments carried out at TREC 2008 Relevance Feedback Track. We focused on ...