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Predicting document effectiveness in pseudo relevance feedback
Proceedings of the 20th ACM international conference on Information and knowledge management, 2011Pseudo relevance feedback (PRF) is one of effective practices in Information Retrieval. In particular, PRF via the relevance model (RM) has been widely used due to the theoretical soundness and effectiveness. In a PRF scenario, an underlying relevance model is inferred by combining language models of the top retrieved documents where the contribution ...
Mostafa Keikha +3 more
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Pseudo Topic Analysis for Boosting Pseudo Relevance Feedback
2019Traditional Pseudo Relevance Feedback (PRF) approaches fail to mode real-world intricate user activities. They naively assume that the first-pass top-ranked search results, i.e. the pseudo relevant set, have potentially relevant aspects for the user query.
Rong Yan, Guanglai Gao
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Pseudo-Relevance Feedback Based on Matrix Factorization
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016In information retrieval, pseudo-relevance feedback (PRF) refers to a strategy for updating the query model using the top retrieved documents. PRF has been proven to be highly effective in improving the retrieval performance. In this paper, we look at the PRF task as a recommendation problem: the goal is to recommend a number of terms for a given query
Hamed Zamani +3 more
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Term Proximity Constraints for Pseudo-Relevance Feedback
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017Pseudo-relevance feedback (PRF) refers to a query expansion strategy based on top-retrieved documents, which has been shown to be highly effective in many retrieval models. Previous work has introduced a set of constraints (axioms) that should be satisfied by any PRF model.
Ali Montazeralghaem +2 more
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Estimation and use of uncertainty in pseudo-relevance feedback
Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 2007Existing pseudo-relevance feedback methods typically perform averaging over the top-retrieved documents, but ignore an important statistical dimension: the risk or variance associated with either the individual document models, or their combination. Treating the baseline feedback method as a black box, and the output feedback model as a random variable,
Kevyn Collins-Thompson, Jamie Callan
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Pseudo-Relevance Feedback Based on mRMR Criteria
2010Pseudo-relevance feedback has shown to be an effective method in many information retrieval tasks. Various criteria have been proposed to rank terms extracted from the top ranked document of the initial retrieval results. However, most existing methods extract terms individually and do not consider the impacts of relationships among terms and their ...
Yuanbin Wu +3 more
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Block-based pseudo-relevance feedback for image retrieval
Journal of Experimental & Theoretical Artificial Intelligence, 2021Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback.
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Social Book Search with Pseudo-Relevance Feedback
2014Massive books with social information, e.g. reviews, rates and tags, have emerged in large numbers on the web. However, there are several limitations in traditional search methods for social books, as social books include complicated and various social information.
Bin Geng +5 more
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Pseudo relevance feedback using semantic clustering in relevance language model
Proceedings of the 18th ACM conference on Information and knowledge management, 2009Pseudo relevance feedback has demonstrated to be in general an effective technique for improving retrieval effectiveness, but the noise in the top retrieved documents still can cause topic drift problem that affects the performance of certain topics.
Qiang Pu, Daqing He
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Evaluation of Pseudo-Relevance Feedback using Wikipedia
Proceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval, 2019Users have specific information needs which are expressed in short queries to information retrieval systems. The queries are unstructured, and they tend to be short and ambiguous in most cases. Using the shallow language statistics including probabilistic or language models such as BM25 or Indri respectively can enhance the retrieval system metrics ...
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