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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
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Patent
30 Sep 1996
TL;DR: In this paper, a method and system for retrieving information in response to a query by a user is presented, which includes the steps of receiving a signal s having a value corresponding to a relevance-ranking algorithm score of a retrieved document, receiving a signals q and v having a values corresponding to the number of words in the query and a signal v corresponding to coordination level of the retrieved document and query (i.e., the degree of overlap between the document terms and the query terms), and generating an adjusted score s1 dependent on the signal s, the signal q and the
Abstract: A method and system for retrieving information in response to a query by a user. The method includes the steps of receiving a signal s having a value corresponding to a relevance-ranking algorithm score of a retrieved document, receiving a signal q having a value corresponding to the number of words in the query and a signal v having a value corresponding to the coordination level of the retrieved document and query (i.e., the degree of overlap between the document terms and the query terms), and generating an adjusted score s1 dependent on the signal s, the signal q and the signal v. The adjusted score s1 takes the coordination level into account for small values of q and gradually decreases the importance of the coordination level as q increases. The system of this invention includes a computer-based system for carrying out the method of this invention.

182 citations

Proceedings ArticleDOI
Dragomir R. Radev1, Weiguo Fan1, Hong Qi1, Harris Wu1, Amardeep Grewal1 
07 May 2002
TL;DR: The architecture that augments existing search engines so that they support natural language question answering, called NSIR, is developed and some probabilistic approaches to the last three of these stages are described.
Abstract: Web-based search engines such as Google and NorthernLight return documents that are relevant to a user query, not answers to user questions. We have developed an architecture that augments existing search engines so that they support natural language question answering. The process entails five steps: query modulation, document retrieval, passage extraction, phrase extraction, and answer ranking. In this paper we describe some probabilistic approaches to the last three of these stages. We show how our techniques apply to a number of existing search engines and we also present results contrasting three different methods for question answering. Our algorithm, probabilistic phrase reranking (PPR) using proximity and question type features achieves a total reciprocal document rank of .20 on the TREC 8 corpus. Our techniques have been implemented as a Web-accessible system, called NSIR.

182 citations

Proceedings ArticleDOI
15 Aug 2005
TL;DR: FLOE is presented, a simple density analysis method for modelling the shape of the transformation required, based on training data and without assuming independence between feature and baseline, for a new query independent feature.
Abstract: A query independent feature, relating perhaps to document content, linkage or usage, can be transformed into a static, per-document relevance weight for use in ranking. The challenge is to find a good function to transform feature values into relevance scores. This paper presents FLOE, a simple density analysis method for modelling the shape of the transformation required, based on training data and without assuming independence between feature and baseline. For a new query independent feature, it addresses the questions: is it required for ranking, what sort of transformation is appropriate and, after adding it, how successful was the chosen transformation? Based on this we apply sigmoid transformations to PageRank, indegree, URL Length and ClickDistance, tested in combination with a BM25 baseline.

182 citations

Journal ArticleDOI
01 Jun 1989
TL;DR: This work aims at developing criteria when re Optimization is required, how these criteria can be implemented efficiently, and how reoptimization can be avoided by using a new technique called dynamic query evaluation plans.
Abstract: In most database systems, a query embedded in a program written in a conventional programming language is optimized when the program is compiled. The query optimizer must make assumptions about the values of the program variables that appear as constants in the query, the resources that can be committed to query evaluation, and the data in the database. The optimality of the resulting query evaluation plan depends on the validity of these assumptions. If a query evaluation plan is used repeatedly over an extended period of time, it is important to determine when reoptimization is necessary. Our work aims at developing criteria when reoptimization is required, how these criteria can be implemented efficiently, and how reoptimization can be avoided by using a new technique called dynamic query evaluation plans. We experimentally demonstrate the need for dynamic plans and outline modifications to the EXODUS optimizer generator required for creating dynamic query evaluation plans.

182 citations

Proceedings ArticleDOI
09 Sep 2012
TL;DR: This paper presents a computationally effective approach for the direct minimization of a ranking objective function, without sampling, and demonstrates by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.
Abstract: Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.

181 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168