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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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Journal ArticleDOI
TL;DR: It is discovered that gender and task significantly influence different kinds of search behaviors discussed here, and this is suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow.
Abstract: To improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users' information needs that goes beyond the queries they type in. Adaptive search engines use explicit and implicit feed-back indicators to model users or search tasks. In order to create appropriate models, it is essential to understand how users interact with search engines, including the determining factors of their actions. Using eye tracking, we extend this understanding by analyzing the sequences and patterns with which users evaluate query result returned to them when using Google. We find that the query result abstracts are viewed in the order of their ranking in only about one fifth of the cases, and only an average of about three abstracts per result page are viewed at all. We also compare search behavior variability with respect to different classes of users and different classes of search tasks to reveal whether user models or task models may be greater predictors of behavior. We discover that gender and task significantly influence different kinds of search behaviors discussed here. The results are suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow.

249 citations

Patent
16 Apr 2002
TL;DR: In this paper, the content of the messages, the recipient's address book, and parameters such as desired keywords and undesired keywords are employed in determining a numeric ranking for each message.
Abstract: Electronic messages are processed based on criteria relating to the sender, the content, and the personalization of the message. The content of the messages, the recipient's address book, and parameters such as desired keywords and undesired keywords are employed in determining a numeric ranking for each message. Based upon the ranking, messages are assigned to a category indicating an expected response of the recipient, such as read, reply, and save, or simply read. Messages in the lowest category (spam) are marked for deletion. Fuzzy logic is preferably applied in determining the category to which a message is assigned based on its ranking. Content importance, sender importance, and degree of personalization are combined in a non-linear manner to rank a message. Based on the recipient's actual response to a message, the priority of subsequent similar messages is adjusted to more accurately assign the messages to a category.

247 citations

Patent
22 May 2007
TL;DR: In this article, the authors present a search experience that is substantially akin to consultation with a human expert, and that satisfies a user's information need in fulfilling projects such as purchasing, shopping, procurement, bartering, requesting for quotes, in online retail, traditional retail, wholesale, health care, travel, real estate, restaurant-going, entertainment, logistics, and sourcing.
Abstract: Methods and apparatus that deliver a searching experience that is substantially akin to consultation with a human expert, and that satisfies a user's information need in fulfilling projects such as purchasing, shopping, procurement, bartering, requesting for quotes, in online retail, traditional retail, wholesale, health care, travel, real estate, restaurant-going, entertainment, logistics, and sourcing are disclosed. Search results often contain entities that provide services and products. Records being searched are associated with industry sectors in a broad sense. Industry sector information is first derived from a user query; and is used in determining relevant and adequate additional questions for a searcher, and in matching, ranking, and presenting search results.

247 citations

Journal ArticleDOI
TL;DR: In this article, the authors examined the information requirements and importance of various types of information for potential students when selecting a university and identified seven broad information categories relating to university selection using data from 306 pupils studying at various schools in England, Scotland and Northern Ireland.
Abstract: This paper aims to examine the information requirements and the importance of various types of information for potential students when selecting a university. Using data from 306 pupils studying at various schools in England, Scotland and Northern Ireland seven broad information categories relating to university selection have been identified. It also revealed that the ranking of the various types of information required and the importance of this information is relatively similar.

246 citations

Proceedings ArticleDOI
01 Jul 2000
TL;DR: An experimental evaluation of link analysis algorithms for their potential to identify high quality items using a dataset of web documents rated for quality by human topic experts found link-based metrics did a good job of picking out high-quality items.
Abstract: For many topics, the World Wide Web contains hundreds or thousands of relevant documents of widely varying quality. Users face a daunting challenge in identifying a small subset of documents worthy of their attention.Link analysis algorithms have received much interest recently, in large part for their potential to identify high quality items. We report here on an experimental evaluation of this potential.We evaluated a number of link and content-based algorithms using a dataset of web documents rated for quality by human topic experts. Link-based metrics did a good job of picking out high-quality items. Precision at 5 is about 0.75, and precision at 10 is about 0.55; this is in a dataset where 0.32 of all documents were of high quality. Surprisingly, a simple content-based metric performed nearly as well; ranking documents by the total number of pages on their containing site.

244 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