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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 Article
19 Jun 2011
TL;DR: This work proposes a method for automatically labelling topics learned via LDA topic models using a combination of association measures and lexical features, optionally fed into a supervised ranking model.
Abstract: We propose a method for automatically labelling topics learned via LDA topic models. We generate our label candidate set from the top-ranking topic terms, titles of Wikipedia articles containing the top-ranking topic terms, and sub-phrases extracted from the Wikipedia article titles. We rank the label candidates using a combination of association measures and lexical features, optionally fed into a supervised ranking model. Our method is shown to perform strongly over four independent sets of topics, significantly better than a benchmark method.

222 citations

Journal ArticleDOI
TL;DR: A novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems, is proposed that significantly outperforms all the state-of-the-art approaches, including both traditional and CNN-based methods on the challenging VIPeR, CUHK-01, and CAVIAR4REID datasets.
Abstract: This paper proposes a novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems. Although a variety of powerful algorithms have been presented in the past few years, most of them usually focus on designing hand-crafted features and learning metrics either individually or sequentially. Different from previous works, we formulate a unified deep ranking framework that jointly tackles both of these key components to maximize their strengths. We start from the principle that the correct match of the probe image should be positioned in the top rank within the whole gallery set. An effective learning-to-rank algorithm is proposed to minimize the cost corresponding to the ranking disorders of the gallery. The ranking model is solved with a deep convolutional neural network (CNN) that builds the relation between input image pairs and their similarity scores through joint representation learning directly from raw image pixels. The proposed framework allows us to get rid of feature engineering and does not rely on any assumption. An extensive comparative evaluation is given, demonstrating that our approach significantly outperforms all the state-of-the-art approaches, including both traditional and CNN-based methods on the challenging VIPeR, CUHK-01, and CAVIAR4REID datasets. In addition, our approach has better ability to generalize across datasets without fine-tuning.

221 citations

Proceedings ArticleDOI
23 Jul 2007
TL;DR: This work focuses on developing a regression framework for learning ranking functions for improving relevance of search engines serving diverse streams of user queries, and proposes a novel optimization framework emphasizing the use of relative relevance judgments.
Abstract: Effective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish two types of relevance judgments used as the training data: 1) absolute relevance judgments arising from explicit labeling of search results; and 2) relative relevance judgments extracted from user click throughs of search results or converted from the absolute relevance judgments. We propose a novel optimization framework emphasizing the use of relative relevance judgments. The main contribution is the development of an algorithm based on regression that can be applied to objective functions involving preference data, i.e., data indicating that a document is more relevant than another with respect to a query. Experimental results are carried out using data sets obtained from a commercial search engine. Our results show significant improvements of our proposed methods over some existing methods.

221 citations

Proceedings ArticleDOI
01 Sep 2001
TL;DR: A new inverted file structure using quantized weights that provides superior retrieval effectiveness compared to conventional inverted file structures when early termination heuristics are employed, and so provide a better cost/performance compromise than previous inverted file organisations.
Abstract: Considerable research effort has been invested in improving the effectiveness of information retrieval systems. Techniques such as relevance feedback, thesaural expansion, and pivoting all provide better quality responses to queries when tested in standard evaluation frameworks. But such enhancements can add to the cost of evaluating queries. In this paper we consider the pragmatic issue of how to improve the cost-effectiveness of searching. We describe a new inverted file structure using quantized weights that provides superior retrieval effectiveness compared to conventional inverted file structures when early termination heuristics are employed. That is, we are able to reach similar effectiveness levels with less computational cost, and so provide a better cost/performance compromise than previous inverted file organisations.

221 citations

Journal ArticleDOI
01 Sep 1997
TL;DR: This work examines links among the nodes returned in a keyword-based query, finding “interesting” sites that are highly connected to those sites returned by the original query by finding ‘hot spots’ on the Web that contain information germane to a user's query.
Abstract: Finding information located somewhere on the World-Wide Web is an error-prone and frustrating task. The WebQuery system offers a powerful new method for searching the Web based on connectivity and content. We do this by examining links among the nodes returned in a keyword-based query. We then rank the nodes, giving the highest rank to the most highly connected nodes. By doing so, we are finding “hot spots” on the Web that contain information germane to a user's query. WebQuery not only ranks and filters the results of a Web query, it also extends the result set beyond what the search engine retrieves, by finding “interesting” sites that are highly connected to those sites returned by the original query. Even with WebQuery filtering and ranking query results, the result sets can be enormous. So, we need to visualize the returned information. We explore several techniques for visualizing this information—including cone trees, 2D graphs, 3D graphs, lists, and bullseyes-and discuss the criteria for using each of the techniques.

221 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