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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10 Nov 2004TL;DR: The need for upload security arises during content sharing between users in communication link with each other and a server as mentioned in this paper, where the server identifies a mobile device that sends an upload message destined to a user.
Abstract: The need for upload security arises during content sharing between users in communication link with each other and a server. In one embodiment, providing the upload security involves the server identifying a mobile device that sends an upload message destined to a user. Providing the upload security further involves the server accessing opt-in parameters predetermined by the user, determining if the identity of the sending mobile device is included in the opt-in parameters, and, if so, allowing the upload to the user's account, otherwise blocking the upload. The opt-in parameters include the identity of mobile devices that are authorized by the user to upload data to the user's account. In one embodiment, the communication link includes a wireless carrier network with capability for security screening of the upload message before it reaches the server based on the identity of the wireless carrier network.
171 citations
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07 Apr 2008TL;DR: In this paper, the problem of efficiently computing diverse query results in online shopping applications was studied, where users specify queries through a form interface that allows a mix of structured and content-based selection conditions.
Abstract: We study the problem of efficiently computing diverse query results in online shopping applications, where users specify queries through a form interface that allows a mix of structured and content-based selection conditions. Intuitively, the goal of diverse query answering is to return a representative set of top-k answers from all the tuples that satisfy the user selection condition. For example, if a user is searching for Honda cars and we can only display five results, we wish to return cars from five different Honda models, as opposed to returning cars from only one or two Honda models. A key contribution of this paper is to formally define the notion of diversity, and to show that existing score based techniques commonly used in web applications are not sufficient to guarantee diversity. Another contribution of this paper is to develop novel and efficient query processing techniques that guarantee diversity. Our experimental results using Yahoo! Autos data show that our proposed techniques are scalable and efficient.
171 citations
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30 Jul 2008TL;DR: In this paper, location-related data and other profile characteristics are used for promotion and for matching of businesses, venues and other entities with user specified criteria, such as location and gender.
Abstract: Systems and methods for profile matching and promotion. Location-related data and other profile characteristics are used for promotion and for matching of businesses, venues and other entities with user specified criteria.
171 citations
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TL;DR: A novel semi-supervised feature selection algorithm, which makes use of both labeled and unlabeled data points, which is compared with Fisher score and Laplacian score on face recognition and demonstrates the efficiency and effectiveness of the algorithm.
171 citations
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TL;DR: The first provably accurate feature selection method for k-means clustering is presented and, in addition, two feature extraction methods are presented that improve upon the existing results in terms of time complexity and number of features needed to be extracted.
Abstract: We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}. A feature selection based algorithm for $k$-means clustering selects a small subset of the input features and then applies $k$-means clustering on the selected features. A feature extraction based algorithm for $k$-means clustering constructs a small set of new artificial features and then applies $k$-means clustering on the constructed features. Despite the significance of $k$-means clustering as well as the wealth of heuristic methods addressing it, provably accurate feature selection methods for $k$-means clustering are not known. On the other hand, two provably accurate feature extraction methods for $k$-means clustering are known in the literature; one is based on random projections and the other is based on the singular value decomposition (SVD).
This paper makes further progress towards a better understanding of dimensionality reduction for $k$-means clustering. Namely, we present the first provably accurate feature selection method for $k$-means clustering and, in addition, we present two feature extraction methods. The first feature extraction method is based on random projections and it improves upon the existing results in terms of time complexity and number of features needed to be extracted. The second feature extraction method is based on fast approximate SVD factorizations and it also improves upon the existing results in terms of time complexity. The proposed algorithms are randomized and provide constant-factor approximation guarantees with respect to the optimal $k$-means objective value.
170 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 |