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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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Proceedings ArticleDOI
21 Aug 2005
TL;DR: A novel approach for using clickthrough data to learn ranked retrieval functions for web search results by using query chains to generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries.
Abstract: This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.

530 citations

Patent
18 Dec 1998
TL;DR: In this paper, the authors present a software facility for identifying the items most relevant to a current query based on items selected in connection with similar queries, and the facility identifies as most relevant those items having the highest ranking values, by combining the relative frequencies with which users selected that item from the query results generated from queries specifying each of the terms specified by the query.
Abstract: The present invention provides a software facility for identifying the items most relevant to a current query based on items selected in connection with similar queries. In preferred embodiments of the invention, the facility receives a query specifying one or more query terms. In response, the facility generates a query result identifying a plurality of items that satisfy the query. The facility then produces a ranking value for at least a portion of the items identified in the query result by combining the relative frequencies with which users selected that item from the query results generated from queries specifying each of the terms specified by the query. The facility identifies as most relevant those items having the highest ranking values.

527 citations

Posted Content
TL;DR: The authors provided a ranking of journals based on their relative influences on the writings of academics, either within the economics profession or in the world at large, and the measurement used to create this ranking was the number of citations that authors make to articles appearing in various journals.
Abstract: A CADEMIC JOURNALS have played an increasingly important role in the dissemination of scientific information throughout this century, particularly during the last decade.1 This fact is no less true in economics than in other disciplines. The number of journals has also increased greatly in recent decades. For these and other reasons, several recent efforts have been made to judge the various qualities and merits of individual journals. Besides being a rather enjoyable form of naval-gazing for those within a given discipline, such activities also provide valuable information. Where articles are published can affect one's promotion, tenure, and salary at one's present job; it can also affect one's brand name and the ability to change jobs. The purpose of this study is to provide a ranking of journals based on their relative influences on the writings of academics, either within the economics profession or in the world at large.2 The measurement used to create this ranking, described in detail below, is the number of citations that authors make to articles appearing in various journals. After a brief discussion of several previous studies, we proceed to a more complete explanation of our procedures and results.

526 citations

Posted Content
TL;DR: In this work, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features.
Abstract: With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels. However, most of these hashing methods are designed to handle simple binary similarity. The complex multilevel semantic structure of images associated with multiple labels have not yet been well explored. Here we propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images. In our approach, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features. Meanwhile, a ranking list that encodes the multilevel similarity information is employed to guide the learning of such deep hash functions. An effective scheme based on surrogate loss is used to solve the intractable optimization problem of nonsmooth and multivariate ranking measures involved in the learning procedure. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods in term of ranking evaluation metrics when tested on multi-label image datasets.

520 citations

Patent
Hinrich Schuetze1
16 Jun 1994
TL;DR: In this paper, a thesaurus of word vectors is formed for the words in the corpus of documents, which represent global lexical co-occurrence patterns and relationships between word neighbors.
Abstract: A method and apparatus accesses relevant documents based on a query. A thesaurus of word vectors is formed for the words in the corpus of documents. The word vectors represent global lexical co-occurrence patterns and relationships between word neighbors. Document vectors, which are formed from the combination of word vectors, are in the same multi-dimensional space as the word vectors. A singular value decomposition is used to reduce the dimensionality of the document vectors. A query vector is formed from the combination of word vectors associated with the words in the query. The query vector and document vectors are compared to determine the relevant documents. The query vector can be divided into several factor clusters to form factor vectors. The factor vectors are then compared to the document vectors to determine the ranking of the documents within the factor cluster.

519 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