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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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Book
06 Jun 2006
TL;DR: Ranking Business Schools: Forming fields, identities and boundaries in international management education as discussed by the authors, which ranks business schools based on their performance in the following three domains: finance, management, and management education.
Abstract: Ranking Business Schools : Forming fields, identities and boundaries in international management education

122 citations

Posted Content
TL;DR: This paper investigates two-branch neural networks for learning the similarity between image-sentence matching and region-phrase matching, and proposes two network structures that produce different output representations.
Abstract: Image-language matching tasks have recently attracted a lot of attention in the computer vision field. These tasks include image-sentence matching, i.e., given an image query, retrieving relevant sentences and vice versa, and region-phrase matching or visual grounding, i.e., matching a phrase to relevant regions. This paper investigates two-branch neural networks for learning the similarity between these two data modalities. We propose two network structures that produce different output representations. The first one, referred to as an embedding network, learns an explicit shared latent embedding space with a maximum-margin ranking loss and novel neighborhood constraints. Compared to standard triplet sampling, we perform improved neighborhood sampling that takes neighborhood information into consideration while constructing mini-batches. The second network structure, referred to as a similarity network, fuses the two branches via element-wise product and is trained with regression loss to directly predict a similarity score. Extensive experiments show that our networks achieve high accuracies for phrase localization on the Flickr30K Entities dataset and for bi-directional image-sentence retrieval on Flickr30K and MSCOCO datasets.

122 citations

Proceedings Article
02 Jun 2010
TL;DR: A system designed for automatically generating personalized annotation tags to label Twitter user's interests and concerns and the user tagging precision is comparable to the precision of keyword extraction from web pages for content-targeted advertising.
Abstract: This paper introduces a system designed for automatically generating personalized annotation tags to label Twitter user's interests and concerns. We applied TFIDF ranking and TextRank to extract keywords from Twitter messages to tag the user. The user tagging precision we obtained is comparable to the precision of keyword extraction from web pages for content-targeted advertising.

122 citations

Proceedings ArticleDOI
15 Aug 2005
TL;DR: This paper studies how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information.
Abstract: Information retrieval is, in general, an iterative search process, in which the user often has several interactions with a retrieval system for an information need. The retrieval system can actively probe a user with questions to clarify the information need instead of just passively responding to user queries. A basic question is thus how a retrieval system should propose questions to the user so that it can obtain maximum benefits from the feedback on these questions. In this paper, we study how a retrieval system can perform active feedback, i.e., how to choose documents for relevance feedback so that the system can learn most from the feedback information. We present a general framework for such an active feedback problem, and derive several practical algorithms as special cases. Empirical evaluation of these algorithms shows that the performance of traditional relevance feedback (presenting the top K documents) is consistently worse than that of presenting documents with more diversity. With a diversity-based selection algorithm, we obtain fewer relevant documents, however, these fewer documents have more learning benefits.

122 citations

Book ChapterDOI
21 Oct 2013
TL;DR: Spark is described, a recommendation engine that links a user's initial query to an entity within a knowledge base and provides a ranking of the related entities and is currently powering Yahoo! Web Search result pages.
Abstract: While some web search users know exactly what they are looking for, others are willing to explore topics related to an initial interest. Often, the user's initial interest can be uniquely linked to an entity in a knowledge base. In this case, it is natural to recommend the explicitly linked entities for further exploration. In real world knowledge bases, however, the number of linked entities may be very large and not all related entities may be equally relevant. Thus, there is a need for ranking related entities. In this paper, we describe Spark, a recommendation engine that links a user's initial query to an entity within a knowledge base and provides a ranking of the related entities. Spark extracts several signals from a variety of data sources, including Yahoo! Web Search, Twitter, and Flickr, using a large cluster of computers running Hadoop. These signals are combined with a machine learned ranking model in order to produce a final recommendation of entities to user queries. This system is currently powering Yahoo! Web Search result pages.

122 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