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.

Frozen shoulder: MR arthrographic findings.

Abstract: PURPOSE: To evaluate the magnetic resonance (MR) arthrographic findings in patients with frozen shoulder. MATERIALS AND METHODS: Preoperative MR arthrograms of 22 patients (six women, 16 men; mean age, 54.7 years) with frozen shoulder treated with arthroscopic capsulotomy were compared with arthrograms of 22 age- and sex-matched control subjects without frozen shoulder. The thickness of the coracohumeral ligament (CHL) and the joint capsule, as well as the volume of the axillary recess, were measured (Mann-Whitney test). Abnormalities in the CHL, subcoracoid fat, superior glenohumeral ligament, superior border of the subscapularis tendon, long biceps tendon, and subscapularis recess were analyzed in consensus by two blinded radiologists (χ2 test). RESULTS: Patients with frozen shoulder had a significantly thickened CHL (4.1 mm vs 2.7 mm in controls) and a thickened joint capsule in the rotator cuff interval (7.1 mm vs 4.5 mm; P < .001 for both comparisons, Mann-Whitney test) but not in the axillary recess...

Identifying authoritative actors in question-answering forums: the case of Yahoo! answers

Abstract: We consider the problem of identifying authoritative users in Yahoo! Answers. A common approach is to use link analysis techniques in order to provide a ranked list of users based on their degree of authority. A major problem for such an approach is determining how many users should be chosen as authoritative from a ranked list. To address this problem, we propose a method for automatic identification of authoritative actors. In our approach, we propose to model the authority scores of users as a mixture of gamma distributions. The number of components in the mixture is estimated by the Bayesian Information Criterion (BIC) while the parameters of each component are estimated using the Expectation-Maximization (EM) algorithm. This method allows us to automatically discriminate between authoritative and non-authoritative users. The suitability of our proposal is demonstrated in an empirical study using datasets from Yahoo! Answers.

Subset ranking using regression

Abstract: We study the subset ranking problem, motivated by its important application in web-search. In this context, we consider the standard DCG criterion (discounted cumulated gain) that measures the quality of items near the top of the rank-list. Similar to error minimization for binary classification, the DCG criterion leads to a non-convex optimization problem that can be NP-hard. Therefore a computationally more tractable approach is needed. We present bounds that relate the approximate optimization of DCG to the approximate minimization of certain regression errors. These bounds justify the use of convex learning formulations for solving the subset ranking problem. The resulting estimation methods are not conventional, in that we focus on the estimation quality in the top-portion of the rank-list. We further investigate the generalization ability of these formulations. Under appropriate conditions, the consistency of the estimation schemes with respect to the DCG metric can be derived.

Experience with personalization of Yahoo

Abstract: COMMUNICATIONS OF THE ACM August 2000/Vol. 43, No. 8 35 In this artcle, we concentrate on three examples of personalization: My Yahoo!, Yahoo! Companion, and Inside Yahoo! Search. My Yahoo! (my.yahoo.com) is a customized personal copy of Yahoo!. Users can select from hundreds of modules, such as news, stock prices, weather, and sports scores, and place them on one or more Web pages. The actual content for each module is then updated automatically, so users can see what they want to see in the order they want to see it. This provides users with the latest information on every subject, but with only the specific items they want to know about. An example of a My Yahoo! page (with Yahoo! Companion) is shown in the accompanying figure. Space limitations prevent us from describing its many features; instead, we mention a few general issues:

Large scale semi-supervised linear SVMs

Abstract: Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In many information retrieval and data mining applications, linear classifiers are strongly preferred because of their ease of implementation, interpretability and empirical performance. In this work, we present a family of semi-supervised linear support vector classifiers that are designed to handle partially-labeled sparse datasets with possibly very large number of examples and features. At their core, our algorithms employ recently developed modified finite Newton techniques. Our contributions in this paper are as follows: (a) We provide an implementation of Transductive SVM (TSVM) that is significantly more efficient and scalable than currently used dual techniques, for linear classification problems involving large, sparse datasets. (b) We propose a variant of TSVM that involves multiple switching of labels. Experimental results show that this variant provides an order of magnitude further improvement in training efficiency. (c) We present a new algorithm for semi-supervised learning based on a Deterministic Annealing (DA) approach. This algorithm alleviates the problem of local minimum in the TSVM optimization procedure while also being computationally attractive. We conduct an empirical study on several document classification tasks which confirms the value of our methods in large scale semi-supervised settings.