Topic
Ranking (information retrieval)
About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.
Papers published on a yearly basis
Papers
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15 Aug 2005TL;DR: This paper proposes several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents.
Abstract: A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.
501 citations
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07 May 2002TL;DR: The experimental results show that the log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods.
Abstract: Query expansion has long been suggested as an effective way to resolve the short query and word mismatching problems A number of query expansion methods have been proposed in traditional information retrieval However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web query logs In this study, we propose a new method for query expansion based on query logs The central idea is to extract probabilistic correlations between query terms and document terms by analyzing query logs These correlations are then used to select high-quality expansion terms for new queries The experimental results show that our log-based probabilistic query expansion method can greatly improve the search performance and has several advantages over other existing methods
495 citations
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01 Aug 1998TL;DR: Investigation into the utility of document summarisation in the context of information retrieval and the application of so called query biased (or user directed) summaries indicate that the use of query biased summaries significantly improves both the accuracy and speed of user relevance judgements.
Abstract: This paper presents an investigation into the utility of document summarisation in the context of information retrieval, more specifically in the application of so called query biased (or user directed) summaries: summaries customised to reflect the information need expressed in a query. Employed in the retrieved document list displayed after a retrieval took place, the summaries' utility was evaluated in a task-based environment by measuring users' speed and accuracy in identifying relevant documents. This was compared to the performance achieved when users were presented with the more typical output of an IR system: a static predefined summary composed of the title and first few sentences of retrieved documents. The results from the evaluation indicate that the use of query biased summaries significantly improves both the accuracy and speed of user relevance judgements.
493 citations
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03 Apr 2017TL;DR: This work proposes a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that Matching with distributed representations complements matching with traditional local representations.
Abstract: Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space. In traditional information retrieval models, on the other hand, terms have discrete or local representations, and the relevance of a document is determined by the exact matches of query terms in the body text. We hypothesize that matching with distributed representations complements matching with traditional local representations, and that a combination of the two is favourable. We propose a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that matches the query and the document using learned distributed representations. The two networks are jointly trained as part of a single neural network. We show that this combination or 'duet' performs significantly better than either neural network individually on a Web page ranking task, and significantly outperforms traditional baselines and other recently proposed models based on neural networks.
489 citations
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TL;DR: The details of the LETOR collection are described and it is shown how it can be used in different kinds of researches, and several state-of-the-art learning to rank algorithms on LETOR are compared.
Abstract: LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.
486 citations