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.
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IBM1
TL;DR: In this article, a method for associating search results is presented, where an original list of search results are provided to a user in response to a first query, and the search results selected by the first user are recorded and associated with the first query.
Abstract: A method for associating search results is provided. According to the method, an original list of search results is provided to a first user in response to a first query, and the search results selected by the first user are recorded and associated with the first query. Additionally, a second query that is the same as or similar to the first query is received from a second user, and an alternate list of search results is provided to the second user. The alternate list lists those search results from the original list that have been associated with the first query due to selection by a user. Also provided is a system for providing search results that includes a search engine, a query database, and a controller. The search engine provides original lists of search results in response to queries, and the query database stores the search results selected by users in response to each of the queries. The controller provides an alternate list of search results in response to another query that is the same as or similar to one of the queries, with the alternate list of search results listing those search results from the original list that have been recorded in the query database as having been previously selected in response to the one query.
127 citations
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19 Jul 2010TL;DR: This paper presents a log-based study estimating the user value of trail following, and compares the relevance, topic coverage, topic diversity, novelty, and utility of full trails over that provided by sub-trails, trail origins, and trail destinations.
Abstract: Search trails mined from browser or toolbar logs comprise queries and the post-query pages that users visit. Implicit endorsements from many trails can be useful for search result ranking, where the presence of a page on a trail increases its query relevance. Follow-ing a search trail requires user effort, yet little is known about the benefit that users obtain from this activity versus, say, sticking with the clicked search result or jumping directly to the destination page at the end of the trail. In this paper, we present a log-based study estimating the user value of trail following. We compare the relevance, topic coverage, topic diversity, novelty, and utility of full trails over that provided by sub-trails, trail origins (landing pages), and trail destinations (pages where trails end). Our findings demonstrate significant value to users in following trails, especially for certain query types. The findings have implications for the design of search systems, including trail recommendation systems that display trails on search result pages.
127 citations
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24 Jul 2011TL;DR: An extension to the Query Likelihood Model is proposed that incorporates query-specific information to estimate rate parameters, and a temporal factor is introduced into language model smoothing and query expansion using pseudo-relevance feedback.
Abstract: Temporal aspects of documents can impact relevance for certain kinds of queries In this paper, we build on earlier work of modeling temporal information We propose an extension to the Query Likelihood Model that incorporates query-specific information to estimate rate parameters, and we introduce a temporal factor into language model smoothing and query expansion using pseudo-relevance feedback We evaluate these extensions using a Twitter corpus and two newspaper article collections Results suggest that, compared to prior approaches, our models are more effective at capturing the temporal variability of relevance associated with some topics
127 citations
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08 May 2001TL;DR: In this article, a similarity score is calculated for the query utilizing a feature vector that characterizes attributes and query words associated with the document, and a rank value is assigned to the document based upon the relevance score and the similarity score.
Abstract: A method of ranking search results includes producing a relevance score for a document in view of a query. A similarity score is calculated for the query utilizing a feature vector that characterizes attributes and query words associated with the document. A rank value is assigned to the document based upon the relevance score and the similarity score.
126 citations
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30 Apr 2000
TL;DR: This paper proposes using sentence-rank-based and content-based measures for evaluating extract summaries, and compares these with recall-based evaluation measures.
Abstract: Summary evaluation measures produce a ranking of all possible extract summaries of a document. Recall-based evaluation measures, which depend on costly human-generated ground truth summaries, produce uncorrelated rankings when ground truth is varied. This paper proposes using sentence-rank-based and content-based measures for evaluating extract summaries, and compares these with recall-based evaluation measures. Content-based measures increase the correlation of rankings induced by synonymous ground truths, and exhibit other desirable properties.
126 citations