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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
Nick Craswell1, Martin Szummer1
23 Jul 2007
TL;DR: A Markov random walk model is applied to a large click log, producing a probabilistic ranking of documents for a given query, demonstrating its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively.
Abstract: Search engines can record which documents were clicked for which query, and use these query-document pairs as "soft" relevance judgments. However, compared to the true judgments, click logs give noisy and sparse relevance information. We apply a Markov random walk model to a large click log, producing a probabilistic ranking of documents for a given query. A key advantage of the model is its ability to retrieve relevant documents that have not yet been clicked for that query and rank those effectively. We conduct experiments on click logs from image search, comparing our ("backward") random walk model to a different ("forward") random walk, varying parameters such as walk length and self-transition probability. The most effective combination is a long backward walk with high self-transition probability.

519 citations

Proceedings ArticleDOI
05 Jul 2008
TL;DR: This work presents two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior and shows that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking.
Abstract: Algorithms for learning to rank Web documents usually assume a document's relevance is independent of other documents. This leads to learned ranking functions that produce rankings with redundant results. In contrast, user studies have shown that diversity at high ranks is often preferred. We present two online learning algorithms that directly learn a diverse ranking of documents based on users' clicking behavior. We show that these algorithms minimize abandonment, or alternatively, maximize the probability that a relevant document is found in the top k positions of a ranking. Moreover, one of our algorithms asymptotically achieves optimal worst-case performance even if users' interests change.

514 citations

Patent
05 May 1999
TL;DR: A system for ranking search results obtained from an information retrieval system includes a search pre-processor, a search engine and a search post-processor as mentioned in this paper, which is used to determine the context of a search query by comparing the terms in the search query with a predetermined user context profile.
Abstract: A system for ranking search results obtained from an information retrieval system includes a search pre-processor, a search engine and a search post-processor. The search preprocessor determines the context of the search query by comparing the terms in the search query with a predetermined user context profile. Preferably, the context profile is a user profile or a community profile, which includes a set of terms which have been rated by the user, community, or a recommender system. The search engine generates a search result comprising at least one item obtained from the information retrieval system. The search post-processor ranks each item returned in the search result in accordance with the context of the search query.

512 citations

Journal ArticleDOI
TL;DR: This work proposes ranking fuzzy numbers with the area between the centroid point and original point to overcome shortcomings in the coefficient of variation (CV index).
Abstract: To improve the ranking method of Lee and Li [1], Cheng [2] proposed the coefficient of variation (CV index). Shortcomings are also found in the CV index. Cheng [2] also proposed the distance method to improve the ranking method of Murakami et al. However, the distance method is not sound either. Moreover, the CV index contradicts the distance method in ranking some fuzzy numbers. Therefore, to overcome the above shortcomings, we propose ranking fuzzy numbers with the area between the centroid point and original point.

512 citations

Tie-Yan Liu, Jun Xu, Tao Qin, Wen-Ying Xiong, Hang Li 
01 Jan 2007
TL;DR: This paper has constructed a benchmark dataset referred to as LETOR, derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data and provided the results of several state-ofthe-arts learning to rank algorithms on the data.
Abstract: This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central problem for information retrieval, and employing machine learning techniques to learn the ranking function is viewed as a promising approach to IR. Unfortunately, there was no benchmark dataset that could be used in comparison of existing learning algorithms and in evaluation of newly proposed algorithms, which stood in the way of the related research. To deal with the problem, we have constructed a benchmark dataset referred to as LETOR and distributed it to the research communities. Specifically we have derived the LETOR data from the existing data sets widely used in IR, namely, OHSUMED and TREC data. The two collections contain queries, the contents of the retrieved documents, and human judgments on the relevance of the documents with respect to the queries. We have extracted features from the datasets, including both conventional features, such as term frequency, inverse document frequency, BM25, and language models for IR, and features proposed recently at SIGIR, such as HostRank, feature propagation, and topical PageRank. We have then packaged LETOR with the extracted features, queries, and relevance judgments. We have also provided the results of several state-ofthe-arts learning to rank algorithms on the data. This paper describes in details about LETOR.

503 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