Institution
Yahoo!
Company•London, United Kingdom•
About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.
Papers published on a yearly basis
Papers
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26 Oct 2010TL;DR: This paper provides a formal treatment on how to represent teams and tasks, and proposes alternative functions for measuring the fitness of a team performing a task and discusses desirable properties of those functions.
Abstract: The internet has enabled the collaboration of groups at a scale that was unseen before. A key problem for large collaboration groups is to be able to allocate tasks effectively. An effective task assignment method should consider both how fit teams are for each job as well as how fair the assignment is to team members, in terms that no one should be overloaded or unfairly singled out. The assignment has to be done automatically or semi-automatically given that it is difficult and time-consuming to keep track of the skills and the workload of each person. Obviously the method to do this assignment must also be computationally efficient. In this paper we present a general framework for task assignment problems. We provide a formal treatment on how to represent teams and tasks. We propose alternative functions for measuring the fitness of a team performing a task and we discuss desirable properties of those functions. Then we focus on one class of task-assignment problems, we characterize the complexity of the problem, and we provide algorithms with provable approximation guarantees, as well as lower bounds. We also present experimental results that show that our methods are useful in practice in several application scenarios.
138 citations
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16 Apr 2012TL;DR: This paper proposes novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations, and develops approximation algorithms for computing a near-optimal set of edges.
Abstract: Content sharing in social networks is a powerful mechanism for discovering content on the Internet. The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes. Existing schemes for recommending connections in social networks are based on the number of common neighbors, similarity of user profiles, etc. However, such similarity-based connections do not consider the amount of content discovered.In this paper, we propose novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations. Unlike existing work on influence propagation, in our environment, we are looking for edges instead of nodes, with a bound on the number of incident edges per node. We show that the content spread function is not submodular, and develop approximation algorithms for computing a near-optimal set of edges. Through experiments on real-world social graphs such as Flickr and Twitter, we show that our approximation algorithms achieve content spreads that are as much as 90 times higher compared to existing heuristics for recommending connections.
138 citations
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21 Oct 2013TL;DR: This paper presents the first experiment that uses eye-tracking to measure the effect of font type on reading speed and presents a set of more accessible fonts for people with dyslexia.
Abstract: Around 10% of the people have dyslexia, a neurological disability that impairs a person's ability to read and write. There is evidence that the presentation of the text has a significant effect on a text's accessibility for people with dyslexia. However, to the best of our knowledge, there are no experiments that objectively measure the impact of the font type on reading performance. In this paper, we present the first experiment that uses eye-tracking to measure the effect of font type on reading speed. Using a within-subject design, 48 subjects with dyslexia read 12 texts with 12 different fonts. Sans serif, monospaced and roman font styles significantly improved the reading performance over serif, proportional and italic fonts. On the basis of our results, we present a set of more accessible fonts for people with dyslexia.
138 citations
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TL;DR: The transumbilical single-port approach is seen as a feasible technique for performing appendectomy that does not increase the rate of complications and represents a possible alternative to conventional laparoscopic appendectomy.
Abstract: Introduction:The use of single-incision laparoscopic surgery may represent an improvement over conventional laparoscopic surgery. In recent years, more and more articles have been published demonstrating the feasibility of this approach. Hence, for this reason, we present this randomized prospective
138 citations
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08 May 2007TL;DR: This work conducts extensive experiments to study the feasibility of using Yahoo! Answers, a general question-answering forum, for human-reviewed data collection, and one such application attempt to provide automated collection of human- reviewed data at Internet-scale.
Abstract: Enterprise and web data processing and content aggregation systems often require extensive use of human-reviewed data (e.g. for training and monitoring machine learning-based applications). Today these needs are often met by in-house efforts or out-sourced offshore contracting. Emerging applications attempt to provide automated collection of human-reviewed data at Internet-scale. We conduct extensive experiments to study the effectiveness of one such application. We also study the feasibility of using Yahoo! Answers, a general question-answering forum, for human-reviewed data collection.
138 citations
Authors
Showing all 26766 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ashok Kumar | 151 | 5654 | 164086 |
Alexander J. Smola | 122 | 434 | 110222 |
Howard I. Maibach | 116 | 1821 | 60765 |
Sanjay Jain | 103 | 881 | 46880 |
Amirhossein Sahebkar | 100 | 1307 | 46132 |
Marc Davis | 99 | 412 | 50243 |
Wenjun Zhang | 96 | 976 | 38530 |
Jian Xu | 94 | 1366 | 52057 |
Fortunato Ciardiello | 94 | 695 | 47352 |
Tong Zhang | 93 | 414 | 36519 |
Michael E. J. Lean | 92 | 411 | 30939 |
Ashish K. Jha | 87 | 503 | 30020 |
Xin Zhang | 87 | 1714 | 40102 |
Theunis Piersma | 86 | 632 | 34201 |
George Varghese | 84 | 253 | 28598 |