Topic
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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TL;DR: It is proved that the MRMF has good generalizability, and develops an efficient optimization algorithm based on the Nesterov method that enforces similar saliency values on neighboringsuperpixels and ranks superpixels according to the learned coefficients.
Abstract: Saliency detection is used to identify the most important and informative area in a scene, and it is widely used in various vision tasks, including image quality assessment, image matching, and object recognition. Manifold ranking (MR) has been used to great effect for the saliency detection, since it not only incorporates the local spatial information but also utilizes the labeling information from background queries. However, MR completely ignores the feature information extracted from each superpixel. In this paper, we propose an MR-based matrix factorization (MRMF) method to overcome this limitation. MRMF models the ranking problem in the matrix factorization framework and embeds query sample labels in the coefficients. By incorporating spatial information and embedding labels, MRMF enforces similar saliency values on neighboring superpixels and ranks superpixels according to the learned coefficients. We prove that the MRMF has good generalizability, and develops an efficient optimization algorithm based on the Nesterov method. Experiments using popular benchmark data sets illustrate the promise of MRMF compared with the other state-of-the-art saliency detection methods.
100 citations
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19 Oct 2009TL;DR: A novel graphical model, regularized Latent Dirichlet Allocation (rLDA), is presented, which facilitates the topic modeling by exploiting both the statistics of tags and visual affinities of images in the corpus.
Abstract: Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many tags are irrelevant to image content. To refine the tags, previous solutions usually mine tag relevance relying on the tag similarity estimated right from the corpus to be refined. The calculation of tag similarity is affected by the noisy tags in the corpus, which is not conducive to estimate accurate tag relevance. In this paper, we propose to do tag refinement from the angle of topic modeling. In the proposed scheme, tag similarity and tag relevance are jointly estimated in an iterative manner, so that they can benefit from each other. Specifically, a novel graphical model, regularized Latent Dirichlet Allocation (rLDA), is presented. It facilitates the topic modeling by exploiting both the statistics of tags and visual affinities of images in the corpus. The experiments on tag ranking and image retrieval demonstrate the advantages of the proposed method.
100 citations
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TL;DR: Despite its shortcomings, Google Scholar Metrics is a helpful tool for authors and editors in identifying core journals and as an increasingly useful tool for ranking scientific journals, it may also challenge established journals products.
Abstract: The launch of Google Scholar Metrics as a tool for assessing scientific journals may be serious competition for Thomson Reuters Journal Citation Reports, and for Scopus powered Scimago Journal Rank. A review of these bibliometric journal evaluation products is performed. We compare their main characteristics from different approaches: coverage, indexing policies, search and visualization, bibliometric indicators, results analysis options, economic cost and differences in their ranking of journals. Despite its shortcomings, Google Scholar Metrics is a helpful tool for authors and editors in identifying core journals. As an increasingly useful tool for ranking scientific journals, it may also challenge established journals products
100 citations
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08 Aug 2005TL;DR: In this paper, a multi-stage query processing system and method enables multistage query scoring, including snippet generation, through incremental document reconstruction facilitated by a multilevel mapping scheme.
Abstract: A multi-stage query processing system and method enables multi-stage query scoring, including “snippet” generation, through incremental document reconstruction facilitated by a multi-tiered mapping scheme. At one or more stages of a multi-stage query processing system a set of relevancy scores are used to select a subset of documents for presentation as an ordered list to a user. The set of relevancy scores can be derived in part from one or more sets of relevancy scores determined in prior stages of the multi-stage query processing system. In some embodiments, the multi-stage query processing system is capable of executing one or more passes on a user query, and using information from each pass to expand the user query for use in a subsequent pass to improve the relevancy of documents in the ordered list.
100 citations
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03 Nov 2014TL;DR: This paper proposes using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem, and proposes a novel framework for lightweight short text classification applications.
Abstract: Most existing approaches for text classification represent texts as vectors of words, namely ``Bag-of-Words.'' This text representation results in a very high dimensionality of feature space and frequently suffers from surface mismatching. Short texts make these issues even more serious, due to their shortness and sparsity. In this paper, we propose using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem. Based on ``Bag-of-Concepts,'' a novel framework is proposed for lightweight short text classification applications. By leveraging a large taxonomy knowledgebase, it learns a concept model for each category, and conceptualizes a short text to a set of relevant concepts. A concept-based similarity mechanism is presented to classify the given short text to the most similar category. One advantage of this mechanism is that it facilitates short text ranking after classification, which is needed in many applications, such as query or ad recommendation. We demonstrate the usage of our proposed framework through a real online application: Channel-based Query Recommendation. Experiments show that our framework can map queries to channels with a high degree of precision (avg. precision=90.3%), which is critical for recommendation applications.
100 citations