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.

From chatter to headlines: harnessing the real-time web for personalized news recommendation

Abstract: We propose a new methodology for recommending interesting news to users by exploiting the information in their twitter persona. We model relevance between users and news articles using a mix of signals drawn from the news stream and from twitter: the profile of the social neighborhood of the users, the content of their own tweet stream, and topic popularity in the news and in the whole twitter-land.We validate our approach on a real-world dataset of approximately 40k articles coming from Yahoo! News and one month of crawled twitter data. We train our model using a learning-to-rank approach and support-vector machines. The train and test set are drawn from Yahoo! toolbar log data. We heuristically identify 3214 users of twitter in the log and use their clicks on news articles to train our system.Our methodology is able to predict with good accuracy the news articles clicked by the users and rank them higher than other news articles. The results show that the combination of various signals from real-time Web and micro-blogging platforms can be a useful resource to understand user behavior.

Simulation of stochastic wind velocity field on long-span bridges

Abstract: An improved algorithm is introduced in this paper for digital simulation of the stochastic wind velocity field on long-span bridges, when the cross-spectral density matrix of the field is given. The target wind velocity field is assumed to be a one-dimensional, multivariate, homogeneous stochastic process. The basic method of simulation used is the spectral representation method. It is improved by explicitly expressing Cholesky's decomposition of the cross-spectral density matrix in the form of algebraic formulas, then cutting off as many as possible of the cosine terms, so long as the accuracy of results is not affected. The fast Fourier transform technique is used to enhance the efficiency of computation. A numerical example of simulation for buffeting analysis is included in this paper to illustrate the improved method introduced. It is demonstrated that deviations between the simulated correlation functions and the target are sufficiently small and that the simulated power spectra are close to the target.

Systems and methods for contextual transaction proposals

Abstract: Context-specific transaction proposals are automatically generated and presented to a user who expresses interest in a particular topic. A user viewing a World Wide Web page or other content item activates an interface to indicate that he or she is interested in additional information related to the subject of the page. A context vector or other representation of the content of the page being viewed is transmitted to an information server, which identifies possible transactions related to the content and proposes one or more of these transactions to the user. Transaction proposals can be presented together with a contextual search interface that allows the user to submit zero or more search terms together with the context vector as a search query.

Boosted Convolutional Neural Networks.

Abstract: In this work, we propose a new algorithm for boosting Deep Convolutional Neural Networks (BoostCNN) to combine the merits of boosting and modern neural networks. To learn this new model, we propose a novel algorithm to incorporate boosting weights into the deep learning architecture based on least squares objective function. We also show that it is possible to use networks of different structures within the proposed boosting framework and BoostCNN is able to select the best network structure in each iteration. This not only results in superior performance but also reduces the required manual effort for finding the right network structure. Experiments show that the proposed method is able to achieve state-of-the-art performance on several fine-grained classification tasks such as bird, car, and aircraft classification.

System and method for advertisement management

Abstract: The present invention relates to systems and methods for optimizing and managing advertising campaigns. The method of the present invention comprises storing one or more advertisement data structures associated with an ad group data structure in the ad group data structure. One or more ad group data structures associated with a campaign data structure are stored in an ad campaign data structure. Additionally, one or more ad campaign data structures associated with an advertised property are stored in an account data structure.