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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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Proceedings ArticleDOI
01 Sep 2001
TL;DR: A probabilistic cross-lingual retrieval system that uses a generative model to estimate the probability that a document in one language is relevant, given a query in another language, which achieves better retrieval results but requires more computation than the structural query translation technique.
Abstract: This work proposes and evaluates a probabilistic cross-lingual retrieval system. The system uses a generative model to estimate the probability that a document in one language is relevant, given a query in another language. An important component of the model is translation probabilities from terms in documents to terms in a query. Our approach is evaluated when 1) the only resource is a manually generated bilingual word list, 2) the only resource is a parallel corpus, and 3) both resources are combined in a mixture model. The combined resources produce about 90% of monolingual performance in retrieving Chinese documents. For Spanish the system achieves 85% of monolingual performance using only a pseudo-parallel Spanish-English corpus. Retrieval results are comparable with those of the structural query translation technique (Pirkola, 1998) when bilingual lexicons are used for query translation. When parallel texts in addition to conventional lexicons are used, it achieves better retrieval results but requires more computation than the structural query translation technique. It also produces slightly better results than using a machine translation system for CLIR, but the improvement over the MT system is not significant.

136 citations

Proceedings ArticleDOI
Haibin Cheng1, Erick Cantú-Paz1
04 Feb 2010
TL;DR: This paper develops user-specific and demographic-based features that reflect the click behavior of individuals and groups in sponsored search and demonstrates that the personalized models significantly improve the accuracy of click prediction.
Abstract: Sponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, filtering and placement have a direct impact on the user experience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pricing impacts the advertisers' return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on observations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform experiments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the personalized models significantly improve the accuracy of click prediction.

136 citations

Patent
Tao Mei1, Xian-Sheng Hua1, Bo Yang1, Linjun Yang1, Shipeng Li1 
26 Jun 2008
TL;DR: In this paper, the authors proposed an automatic video recommendation system using multimodal features (e.g., visual, aural and textural) extracted from the videos for more reliable relevance ranking.
Abstract: Automatic video recommendation is described. The recommendation does not require an existing user profile. The source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation. The comparison is performed with respect to a weighted feature set including at least one content-based feature, such as a visual feature, an aural feature and a content-derived textural feature. Multimodal implementation including multimodal features (e.g., visual, aural and textural) extracted from the videos is used for more reliable relevance ranking. One embodiment uses an indirect textural feature generated by automatic text categorization based on a set of predefined category hierarchy. Another embodiment uses self-learning based on user click-through history to improve relevance ranking.

136 citations

Book ChapterDOI
11 Jul 2005
TL;DR: This work defines lexicographic polyranking functions in the context of loops with parallel transitions consisting of polynomial assertions, including inequalities, over primed and unprimed variables and addresses synthesis of these functions with a complete and automatic method for synthesizing Lexicographic linear poly ranking functions with supporting linear invariants over linear loops.
Abstract: Although every terminating loop has a ranking function, not every loop has a ranking function of a restricted form, such as a lexicographic tuple of polynomials over program variables. The polyranking principle is proposed as a generalization of polynomial ranking for analyzing termination of loops. We define lexicographic polyranking functions in the context of loops with parallel transitions consisting of polynomial assertions, including inequalities, over primed and unprimed variables. Next, we address synthesis of these functions with a complete and automatic method for synthesizing lexicographic linear polyranking functions with supporting linear invariants over linear loops.

136 citations

Patent
29 Apr 2002
TL;DR: In this article, the search results display area and topic word display area adjacently on a retrieval assisting interface, the title information and topic information can be browsed by users; by arranging search results analysis means such as mark title button for emphasizing documents containing designated topic words, along with mark topic word button for emphasis topic words contained in a designated document, users can analyze search results readily from various standpoints.
Abstract: Achieving efficient analysis of search results, which is required for the examination of search queries, by listing up both title information of a retrieved document group and the whole information. By arranging search results display area and topic word display area adjacently on a retrieval assisting interface, the title information and topic information can be browsed by users; by arranging search results analysis means such as mark title button for emphasizing documents containing designated topic words, along with mark topic word button for emphasizing topic words contained in a designated document, users can analyze search results readily from various standpoints.

136 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