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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 Article
01 Dec 2008
TL;DR: An answer ranking engine for non-factoid questions built using a large online community-generated question-answer collection (Yahoo! Answers) is described and it is demonstrated that using them in combination leads to considerable improvements in accuracy.
Abstract: This work describes an answer ranking engine for non-factoid questions built using a large online community-generated question-answer collection (Yahoo! Answers). We show how such collections may be used to effectively set up large supervised learning experiments. Furthermore we investigate a wide range of feature types, some exploiting NLP processors, and demonstrate that using them in combination leads to considerable improvements in accuracy.

258 citations

Journal ArticleDOI
TL;DR: A revised method is proposed which can avoid problems of Chu and Tsao's method for ranking fuzzy numbers, and it is easy to rank fuzzy numbers in a way similar to the original method.
Abstract: In 2002, Chu and Tsao proposed a method to rank fuzzy numbers. They employed an area between the centroid and original points to rank fuzzy numbers; however there were some problems with the ranking method. In this paper, we want to indicate these problems of Chu and Tsao's method, and then propose a revised method which can avoid these problems for ranking fuzzy numbers. Since the revised method is based on the Chu and Tsao's method, it is easy to rank fuzzy numbers in a way similar to the original method.

258 citations

Proceedings ArticleDOI
19 Jul 2009
TL;DR: A novel positional language model (PLM) is proposed which implements both heuristics in a unified language model and is effective for passage retrieval and performs better than a state-of-the-art proximity-based retrieval model.
Abstract: Although many variants of language models have been proposed for information retrieval, there are two related retrieval heuristics remaining "external" to the language modeling approach: (1) proximity heuristic which rewards a document where the matched query terms occur close to each other; (2) passage retrieval which scores a document mainly based on the best matching passage. Existing studies have only attempted to use a standard language model as a "black box" to implement these heuristics, making it hard to optimize the combination parameters. In this paper, we propose a novel positional language model (PLM) which implements both heuristics in a unified language model. The key idea is to define a language model for each position of a document, and score a document based on the scores of its PLMs. The PLM is estimated based on propagated counts of words within a document through a proximity-based density function, which both captures proximity heuristics and achieves an effect of "soft" passage retrieval. We propose and study several representative density functions and several different PLM-based document ranking strategies. Experiment results on standard TREC test collections show that the PLM is effective for passage retrieval and performs better than a state-of-the-art proximity-based retrieval model.

257 citations

Proceedings Article
01 Jan 2018
TL;DR: In this paper, a new technique for learning visual-semantic embeddings for cross-modal retrieval is proposed, inspired by hard negative mining, the use of hard negatives in structured prediction, and ranking loss functions.
Abstract: We present a new technique for learning visual-semantic embeddings for cross-modal retrieval. Inspired by hard negative mining, the use of hard negatives in structured prediction, and ranking loss functions, we introduce a simple change to common loss functions used for multi-modal embeddings. That, combined with fine-tuning and use of augmented data, yields significant gains in retrieval performance. We showcase our approach, VSE++, on MS-COCO and Flickr30K datasets, using ablation studies and comparisons with existing methods. On MS-COCO our approach outperforms state-of-the-art methods by 8.8% in caption retrieval and 11.3% in image retrieval (at R@1).

257 citations

Patent
John S. Breese1, David Heckerman1, Eric Horvitz1, Carl M. Kadie1, Keiji Kanazawa1 
28 Feb 1997
TL;DR: In this paper, the authors proposed a method to reduce or eliminate the risk of locating known information near the top of a list of search results by discounting the ranking, or adjusting ranking values generated by a known search engine as a function of the knowledge probability estimates.
Abstract: Information retrieval methods and apparatus which involve: 1) the generation of estimates regarding the probability that items included in search results are already known to the user and 2) the use of such knowledge probability estimates to influence the ranking of search results, are described. By discounting the ranking, or adjusting ranking values generated by a known search engine as a function of the knowledge probability estimates, the present invention reduces or eliminates the risk of locating known information near the top of a list of search results. This is advantageous since known information is generally of little interest to a user. In various embodiments the popularity of an item is used to estimate the probability that the item is already known to a user. In addition, in various embodiments one or more user controllable parameters are used in the generation of the knowledge probability estimates and/or the ranking of the search results to give the user an opportunity to have the ranking of the search results accurately reflect the user's knowledge. The present invention is particularly well suited to collaborative filtering based search systems. This is because collaborative filters make recommendations to a user based on historical information relating to, e.g., the popularity of items being considered for recommendation. This same popularity information can be used to estimate a users knowledge of a database item. Such items may include television shows, music, Internet sites, etc.

257 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