scispace - formally typeset
Search or ask a question
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
More filters
Patent
Wen-Syan Li1, Quoc Vu1
22 Mar 1999
TL;DR: In this article, the authors present a hypermedia database for managing bookmarks, which allows a user to organize hypertext documents for querying, navigating, sharing and viewing, and also provides access control to the information in the database.
Abstract: The present invention provides a hypermedia database for managing bookmarks, which allows a user to organize hypertext documents for querying, navigating, sharing and viewing. In addition, the hypermedia database also provides access control to the information in the database. The hypermedia database of the present invention parses meta-data from bookmarked documents and indexes and classifies the documents. The present invention supports advanced query and navigation of a collection of bookmarks, especially providing various personalized bookmark services. In one embodiment, the present invention utilizes a proxy server to observe a user's access patterns to provide useful personalized services, such as automated URL bookmarking, document refresh, and bookmark expiration. In addition, a user may also specify various preference in bookmark management, e.g., ranking schemes (i.e. by referral, access frequency, or popularity) and navigation tree fan-out. A subscription service which retrieves new or updated documents of user-specified interests is also provided.

428 citations

Journal ArticleDOI
01 Feb 2007
TL;DR: This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework that incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets.
Abstract: This correspondence presents a novel hybrid wrapper and filter feature selection algorithm for a classification problem using a memetic framework. It incorporates a filter ranking method in the traditional genetic algorithm to improve classification performance and accelerate the search in identifying the core feature subsets. Particularly, the method adds or deletes a feature from a candidate feature subset based on the univariate feature ranking information. This empirical study on commonly used data sets from the University of California, Irvine repository and microarray data sets shows that the proposed method outperforms existing methods in terms of classification accuracy, number of selected features, and computational efficiency. Furthermore, we investigate several major issues of memetic algorithm (MA) to identify a good balance between local search and genetic search so as to maximize search quality and efficiency in the hybrid filter and wrapper MA

426 citations

Proceedings ArticleDOI
12 Dec 2016
TL;DR: A deep tripletranking model for instance-level SBIR is developed with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data.
Abstract: We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) free-hand (finger) sketches are highly abstract, making fine-grained matching harder, and most importantly (iii) annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketchphoto pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep tripletranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for finegrained cross-domain ranking tasks.

420 citations

Proceedings Article
07 Sep 1999
TL;DR: This paper describes the query processor of Lore, a DBMS for XML-based data supporting an expressive query language and focuses primarily on Lore's cost-based query optimizer, including heuristics for reducing the large search space.
Abstract: XML is an emerging standard for data representation and exchange on the World-Wide Web. Due to the nature of information on the Web and the inherent flexibility of XML, we expect that much of the data encoded in XML will be semistructured: the data may be irregular or incomplete, and its structure may change rapidly or unpredictably. This paper describes the query processor of Lore, a DBMS for XML-based data supporting an expressive query language. We focus primarily on Lore's cost-based query optimizer. While all of the usual problems associated with cost-based query optimization apply to XML-based query languages, a number of additional problems arise, such as new kinds of indexing, more complicated notions of database statistics, and vastly different query execution strategies for different databases. We define appropriate logical and physical query plans, database statistics, and a cost model, and we describe plan enumeration including heuristics for reducing the large search space. Our optimizer is fully implemented in Lore and preliminary performance results are reported. This is a short version of the paper Query Optimization for Semistructured Data which is available at: http://www-db.stanford.edu/~mchughj/publications/qo.ps

419 citations

Proceedings ArticleDOI
TL;DR: A unified framework takes advantage of both schools of thinking in information retrieval modelling and shows that the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model to achieve a better estimation for document ranking.
Abstract: This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.

416 citations


Network Information
Related Topics (5)
Web page
50.3K papers, 975.1K citations
83% related
Ontology (information science)
57K papers, 869.1K citations
82% related
Graph (abstract data type)
69.9K papers, 1.2M citations
82% related
Feature learning
15.5K papers, 684.7K citations
81% related
Supervised learning
20.8K papers, 710.5K citations
81% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168