Yahoo!

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.

Large scale multi-label classification via metalabeler

Abstract: The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.

Probabilistic databases: diamonds in the dirt

Abstract: A wide range of applications have recently emerged that need to manage large, imprecise data sets. The reasons for imprecision in data are as diverse as the applications themselves: in sensor and RFID data, imprecision is due to measurement errors [15, 34]; in information extraction, imprecision comes from the inherent ambiguity in natural-language text [20, 26]; and in business intelligence, imprecision is tolerated because of the high cost of data cleaning [5]. In some applications, such as privacy, it is a requirement that the data be less precise. For example, imprecision is purposely inserted to hide sensitive attributes of individuals so that the data may be published [30]. Imprecise data has no place in traditional, precise database applications like payroll and inventory, and so, current database management systems are not prepared to deal with it. In contrast, the newly emerging applications offer value precisely because they query, search, and aggregate large volumes of imprecise data to find the “diamonds in the dirt”. This wide-variety of new applications points to the need for generic tools to manage imprecise data. In this paper, we survey the state of the art of techniques that handle imprecise data, by modeling it as probabilistic data [2–4,7,12,15,23,27,36]. A probabilistic database management system, or ProbDMS, is a system that stores large volumes of probabilistic data and supports complex queries. A ProbDMS may also need to perform some additional tasks, such as updates or recovery, but these do not differ from those in conventional database management systems and will not be discussed here. The major challenge in a ProbDMS is that it needs both to scale to large data volumes, a core competence of database management systems, and to do probabilistic inference, which is a problem studied in AI. While many scalable data management systems exists, probabilistic inference is a hard problem [35], and current systems do not scale to the same extent as data management systems do. To address this challenge, researchers have focused on the specific

Determining mobile content for a social network based on location and time

Abstract: A device, system, and method are directed towards updating location information for a social network. A request for the location information associated with a GPS coordinate is received from a client device associated with a member of the social network. In response to the request, a location name associated with another member of the social network is provided to the client device based on the GPS coordinate. Another location name may be received from the client device. The other location name may be associated with the GPS coordinate and with the member in the social network. Thus, the GPS coordinate and/or member may be associated with a plurality of location names. A location description may also be received and associated with the location name and with the member in the social network.

Contextual Bandit Algorithms with Supervised Learning Guarantees

Abstract: We address the problem of competing with any large set of N policies in the nonstochastic bandit setting, where the learner must repeatedly select among K actions but observes only the reward of the chosen action.

Clinical performance of a Durasul highly cross-linked polyethylene acetabular liner for total hip arthroplasty at five years.

Abstract: Background: Highly cross-linked polyethylene is currently the most common articulation surface used for total hip arthroplasty. The hypothesis of the present study was that the Durasul highly cross-linked polyethylene acetabular liner would have less wear at five years than would a conventional polyethylene liner used in association with the same total hip replacement system. Methods: Forty-three consecutive patients (fifty hips) underwent total hip replacement with an uncemented titanium porous-coated metal cup and a Durasul liner that was mated with a 28-mm cobalt-chromium femoral head. Thirty-one patients (thirty-seven hips) were followed for at least five years. Thirty-five other patients (thirty-seven hips) underwent total hip arthroplasty with the same system but with a conventional polyethylene liner, and these patients also were followed for five years. Clinical assessment was performed with use of the Harris hip score and a patient self-assessment examination. Radiographic analysis included measurements of acetabular component position, fixation, and osteolysis. Femoral head penetration of the Durasul liners was compared with that of the conventional liners. Results: The clinical results as determined on the basis of Harris hip scores and patient self-assessment examinations did not differ between the Durasul group and the control group. The mean bedding-in penetration was 0.054 ± 0.07 mm for the Durasul group and 0.059 ± 0.154 mm for the control group. The subsequent penetration, with elimination of the bedding-in wear, resulted in a linear wear rate of 0.029 ± 0.02 mm per year for the Durasul group, compared with 0.065 ± 0.03 mm per year for the control group (p < 0.005). The annual penetration at one and five years was 0.074 mm and 0.011 mm, respectively, for the Durasul group, compared with 0.151 mm and 0.04 mm, respectively, for the control group. Conclusions: While the qualitative wear pattern of the highly cross-linked polyethylene liner was the same as that of the conventional polyethylene liner, the annual linear wear rate was 45% of that seen with the conventional polyethylene liner. Therefore, we believe that these early data support the continued use of this highly cross-linked polyethylene liner for total hip arthroplasty. Level of Evidence: Therapeutic Level III. See Instructions to Authors for a complete description of levels of evidence.