Results 211 to 220 of about 261,614 (261)
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Collaborative pseudo-relevance feedback
Expert Systems With Applications, 2013Pseudo-relevance feedback (PRF) is a technique commonly used in the field of information retrieval. The performance of PRF is heavily dependent upon parameter values. When relevance judgements are unavailable, these parameters are difficult to set. In the following paper, we introduce a novel approach to PRF inspired by collaborative filtering (CF). We
Dong Zhou, Jianxun Liu
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Relevance feedback in Surfimage
Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201), 2002Relevance feedback is one of the strong components of Surfimage, the INRIA content-based image retrieval system. Relevance feedback is about learning from user interaction, and is useful in tasks like query refinement and multiple queries. We present two relevance feedback techniques currently implemented in Surfimage.
Christophe Meilhac +2 more
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Relevance Feedback with Brain Signals
ACM Transactions on Information SystemsThe Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on the user, extensive studies have explored using pseudo-relevance signals and implicit feedback signals as substitutes.
Ziyi Ye, Xiaohui Xie, Qingyao Ai
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Image retrieval with relevance feedback
Proceedings 29th Applied Imagery Pattern Recognition Workshop, 2002The proposed system for image retrieval using multidimensional features (IRMF) characterises and matches image content in a high dimensional feature space of colour, texture and shape dimensions. By including the entire pyramid of low-, medium-, and high-level primitives, the semantics of image content at different feature levels can be represented and
Li Fang, Yew-Hock Ang
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Diversified relevance feedback
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013The need for a search engine to deal with ambiguous queries has been known for a long time (diversification). However, it is only recently that this need has become a focus within information retrieval research. How to respond to indications that a result is relevant to a query (relevance feedback) has also been a long focus of research.
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Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '92, 1992
Researchers have found relevance feedback to be effective in interactive information retrieval, although few formal user experiments have been made. In order to run a user experiment on a large document collection, experiments were performed at NIST to complete some of the missing links found in using the probabilistic retrieval model.
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Researchers have found relevance feedback to be effective in interactive information retrieval, although few formal user experiments have been made. In order to run a user experiment on a large document collection, experiments were performed at NIST to complete some of the missing links found in using the probabilistic retrieval model.
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Incremental relevance feedback
Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '92, 1992Although relevance feedback techniques have been investigated for more than 20 years, hardly any of these techniques has been implemented in a commercial full-text document retrieval system. In addition to pure performance problems, this is due to the fact that the application of relevance feedback techniques increases the complexity of the user ...
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On bias problem in relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management, 2011Relevance feedback is an effective approach to improve retrieval quality over the initial query. Typical relevance feedback methods usually select top-ranked documents for relevance judgments, then query expansion or model updating are carried out based on the feedback documents.
Qianli Xing 0001 +2 more
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ACM Transactions on Information Systems, 2019
Document retrieval methods that utilize relevance feedback often induce a single query model from the set of feedback documents, specifically, the relevant documents. We empirically show that for a few state-of-the-art query-model induction methods, retrieval performance can be significantly improved by constructing the query model from a subset of the
Fiana Raiber, Oren Kurland
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Document retrieval methods that utilize relevance feedback often induce a single query model from the set of feedback documents, specifically, the relevant documents. We empirically show that for a few state-of-the-art query-model induction methods, retrieval performance can be significantly improved by constructing the query model from a subset of the
Fiana Raiber, Oren Kurland
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Relevance feedback and inference networks
Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '93, 1993Relevance feedback, which modifies queries using judgements of the relevance of a few, highly-ranked documents, has historically been an important method for increasing the performance of information retrieval systems. In this paper, we extend the inference network model introduced by Turtle and Croft to include relevance feedback techniques.
David Haines, W. Bruce Croft
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