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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.


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Proceedings Article
09 Jan 1998
TL;DR: An information extraction system was adapted to act as a post-filter on the output of an IR system to improve precision on routing tasks and make it easier to write IE grammars for multiple topics.
Abstract: : The authors describe an approach to applying a particular kind of Natural Language Processing (NLP) system to the TREC routing task in Information Retrieval (IR). Rather than attempting to use NLP techniques in indexing documents in a corpus, they adapted an information extraction (IE) system to act as a post-filter on the output of an IR system. The IE system was configured to score each of the top 2000 documents as determined by an IR system and on the basis of that score to rerank those 2000 documents. One aim was to improve precision on routing tasks. Another was to make it easier to write IE grammars for multiple topics.

189 citations

Journal ArticleDOI
TL;DR: A context-aware sensor search, selection, and ranking model, called CASSARAM, is proposed to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities.
Abstract: The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating “things” or Internet Connected Objects (ICOs) that will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection, and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM considers user preferences and a broad range of sensor characteristics such as reliability, accuracy, location, battery life, and many more. This paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This paper also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.

189 citations

Proceedings Article
16 Jun 2013
TL;DR: If an average of O(n log(n) binary comparisons are measured, then one algorithm recovers the true ranking in a uniform sense, while the other predicts the ranking more accurately near the top than the bottom.
Abstract: The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking, biological applications and others. In many practical situations the true pairwise comparisons cannot be actively measured, but a subset of all n(n-1)/2 comparisons is passively and noisily observed. Optimization algorithms (e.g., the SVM) could be used to predict a ranking with fixed expected Kendall tau distance, while achieving an Ω (n) lower bound on the corresponding sample complexity. However, due to their centralized structure they are difficult to extend to online or distributed settings. In this paper we show that much simpler algorithms can match the same Ω (n) lower bound in expectation. Furthermore, if an average of O(n log(n)) binary comparisons are measured, then one algorithm recovers the true ranking in a uniform sense, while the other predicts the ranking more accurately near the top than the bottom. We discuss extensions to online and distributed ranking, with benefits over traditional alternatives.

189 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data and reveals that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.
Abstract: Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

189 citations

Proceedings ArticleDOI
24 Oct 2011
TL;DR: It is shown how reading level can provide a valuable new relevance signal for both general and personalized Web search, and models and algorithms are described to address the three key problems in improving relevance for search using reading difficulty.
Abstract: Traditionally, search engines have ignored the reading difficulty of documents and the reading proficiency of users in computing a document ranking. This is one reason why Web search engines do a poor job of serving an important segment of the population: children. While there are many important problems in interface design, content filtering, and results presentation related to addressing children's search needs, perhaps the most fundamental challenge is simply that of providing relevant results at the right level of reading difficulty. At the opposite end of the proficiency spectrum, it may also be valuable for technical users to find more advanced material or to filter out material at lower levels of difficulty, such as tutorials and introductory texts. We show how reading level can provide a valuable new relevance signal for both general and personalized Web search. We describe models and algorithms to address the three key problems in improving relevance for search using reading difficulty: estimating user proficiency, estimating result difficulty, and re-ranking based on the difference between user and result reading level profiles. We evaluate our methods on a large volume of Web query traffic and provide a large-scale log analysis that highlights the importance of finding results at an appropriate reading level for the user.

188 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