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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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Journal Article
TL;DR: In this article, the spectral properties of the Laplacian of the features' measurement matrix are used to define relevance function and the feature selection process is then based on a continuous ranking of features defined by a least-squares optimization process.
Abstract: The problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science Examples in computer vision, text processing and more recently bio-informatics are abundant In text classification tasks, for example, it is not uncommon to have 104 to 107 features of the size of the vocabulary containing word frequency counts, with the expectation that only a small fraction of them are relevant Typical examples include the automatic sorting of URLs into a web directory and the detection of spam emailIn this work we present a definition of "relevancy" based on spectral properties of the Laplacian of the features' measurement matrix The feature selection process is then based on a continuous ranking of the features defined by a least-squares optimization process A remarkable property of the feature relevance function is that sparse solutions for the ranking values naturally emerge as a result of a "biased non-negativity" of a key matrix in the process As a result, a simple least-squares optimization process converges onto a sparse solution, ie, a selection of a subset of features which form a local maximum over the relevance function The feature selection algorithm can be embedded in both unsupervised and supervised inference problems and empirical evidence show that the feature selections typically achieve high accuracy even when only a small fraction of the features are relevant

196 citations

Proceedings ArticleDOI
15 Apr 2007
TL;DR: The Lower Bound Constraint algorithm (LBC) is proven to be an instance optimal algorithm and extensive experiments demonstrate that LBC is four times more efficient than a straightforward algorithm.
Abstract: Skyline query processing has been investigated extensively in recent years, mostly for only one query reference point. An example of a single-source skyline query is to find hotels which are cheap and close to the beach (an absolute query), or close to a user-given location (a relatively query). A multi-source skyline query considers several query points at the same time (e.g., to find hotels which are cheap and close to the University, the Botanic Garden and the China Town). In this paper, we consider the problem of efficient multi-source skyline query processing in road networks. It is not only the first effort to consider multi-source skyline query in road networks but also the first effort to process the relative skyline queries where the network distance between two locations needs to be computed on-the-fly. Three different query processing algorithms are proposed and evaluated in this paper. The Lower Bound Constraint algorithm (LBC) is proven to be an instance optimal algorithm. Extensive experiments using large real road network datasets demonstrate that LBC is four times more efficient than a straightforward algorithm.

195 citations

Proceedings Article
11 Jul 2002
TL;DR: This paper shows the instability of the manual evaluation of summaries, and investigates the feasibility of automated summary evaluation based on the recent BLEU method from machine translation using accumulative n-gram overlap scores between system and human summaries.
Abstract: In this paper we discuss manual and automatic evaluations of summaries using data from the Document Understanding Conference 2001 (DUC-2001). We first show the instability of the manual evaluation. Specifically, the low interhuman agreement indicates that more reference summaries are needed. To investigate the feasibility of automated summary evaluation based on the recent BLEU method from machine translation, we use accumulative n-gram overlap scores between system and human summaries. The initial results provide encouraging correlations with human judgments, based on the Spearman rank-order correlation coefficient. However, relative ranking of systems needs to take into account the instability.

195 citations

Proceedings ArticleDOI
24 Oct 2011
TL;DR: This paper proposes a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward, and investigates how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets.
Abstract: The increasing volume of streaming data on microblogs has re-introduced the necessity of effective filtering mechanisms for such media. Microblog users are overwhelmed with mostly uninteresting pieces of text in order to access information of value. In this paper, we propose a personalized tweet ranking method, leveraging the use of retweet behavior, to bring more important tweets forward. In addition, we also investigate how to determine the audience of tweets more effectively, by ranking the users based on their likelihood of retweeting the tweets. Finally, conducting a pilot user study, we analyze how retweet likelihood correlates with the interestingness of the tweets.

195 citations

Journal ArticleDOI
TL;DR: Results demonstrated that the proposed interactive 3-D object retrieval scheme not only significantly speeds up the retrieval process but also achieves encouraging retrieval performance.
Abstract: The explosively increasing 3-D objects make their efficient retrieval technology highly desired. Extensive research efforts have been dedicated to view-based 3-D object retrieval for its advantage of using 2-D views to represent 3-D objects. In this paradigm, typically the retrieval is accomplished by matching the views of the query object with the objects in database. However, using all the query views may not only introduce difficulty in rapid retrieval but also degrade retrieval accuracy when there is a mismatch between the query views and the object views in the database. In this work, we propose an interactive 3-D object retrieval scheme. Given a set of query views, we first perform clustering to obtain several candidates. We then incrementally select query views for object matching: in each round of relevance feedback, we only add the query view that is judged to be the most informative one based on the labeling information. In addition, we also propose an efficient approach to learn a distance metric for the newly selected query view and the weights for combining all of the selected query views. We conduct experiments on the National Taiwan University 3D Model database, ETH 3D object collection, and Shape Retrieval Content of Non-Rigid 3D Model, and results demonstrated that our approach not only significantly speeds up the retrieval process but also achieves encouraging retrieval performance.

195 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