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.

Personalized social recommendations: accurate or private

Abstract: With the recent surge of social networks such as Facebook, new forms of recommendations have become possible -- recommendations that rely on one's social connections in order to make personalized recommendations of ads, content, products, and people. Since recommendations may use sensitive information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing trade-offs between accuracy and privacy of personalized social recommendations.We study whether "social recommendations", or recommendations that are solely based on a user's social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We then adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to our lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.

Monounsaturated Fat–Rich Diet Prevents Central Body Fat Distribution and Decreases Postprandial Adiponectin Expression Induced by a Carbohydrate-Rich Diet in Insulin-Resistant Subjects

Abstract: OBJECTIVE — Central obesity is associated with insulin resistance through factors that are not fully understood. We studied the effects of three different isocaloric diets on body fat distribution, insulin sensitivity, and peripheral adiponectin gene expression. RESEARCH DESIGN AND METHODS — Eleven volunteers, offspring of obese type 2 diabetic patients with abdominal fat deposition, were studied. These subjects were considered insulin resistant as indicated by Matsuda index values 1 ) diet enriched in saturated fat (SAT), 2 ) diet rich in monounsaturated fat (MUFA) (Mediterranean diet), and 3 ) diet rich in carbohydrates (CHOs). RESULTS — Weight, body composition, and resting energy expenditure remained unchanged during the three sequential dietary periods. Using dual-energy X-ray absorptiometry we observed that when patients were fed a CHO-enriched diet, their fat mass was redistributed toward the abdominal depot, whereas periphery fat accumulation decreased compared with isocaloric MUFA-rich and high-SAT diets (ANOVA P P CONCLUSIONS — An isocaloric MUFA-rich diet prevents central fat redistribution and the postprandial decrease in peripheral adiponectin gene expression and insulin resistance induced by a CHO-rich diet in insulin-resistant subjects.

Fast and Space-Efficient Entity Linking for Queries

Abstract: Entity linking deals with identifying entities from a knowledge base in a given piece of text and has become a fundamental building block for web search engines, enabling numerous downstream improvements from better document ranking to enhanced search results pages. A key problem in the context of web search queries is that this process needs to run under severe time constraints as it has to be performed before any actual retrieval takes place, typically within milliseconds.In this paper we propose a probabilistic model that leverages user-generated information on the web to link queries to entities in a knowledge base. There are three key ingredients that make the algorithm fast and space-efficient. First, the linking process ignores any dependencies between the different entity candidates, which allows for a O(k2) implementation in the number of query terms. Second, we leverage hashing and compression techniques to reduce the memory footprint. Finally, to equip the algorithm with contextual knowledge without sacrificing speed, we factor the distance between distributional semantics of the query words and entities into the model.We show that our solution significantly outperforms several state-of-the-art baselines by more than 14% while being able to process queries in sub-millisecond times---at least two orders of magnitude faster than existing systems.

Multicriteria selection of project managers by applying grey criteria

Abstract: There is a number of criteria and associated sub‐criteria influencing the match of managers to construction projects. Criteria and sub‐criteria were identified based on a thorough review of the related literature and interviews of management personnel involved in the project managers selection. Project managers characteristics are considered to be less important for an effective project management. The model is based on multicriteria evaluation of project managers. The evaluation embraces the identified criteria influencing the process of construction project manager selection. This paper considers the application of grey relations methodology to defining the utility of alternatives, and offers a multiple criteria method of Complex Proportional Assessment of alternatives with grey relations (COPRAS‐G) for analysis. In this model, the parameters of the alternatives are determined by the grey relational grade and expressed in terms of intervals. A case study presents the selection of construction p...

Method and apparatus for search ranking using human input and automated ranking

Abstract: A search system provides search results to searchers in response to search queries and the search results are ranked. The ranking is determined by an automated ranking process in combination with human editorial input. A search system might comprise a query server for receiving a current query, a corpus of documents to which the current query is applied, ranking data storage for storing information from an editorial session involving a human editor and a reviewed query at least similar to the current query, and a rank adjuster for generating a ranking of documents returned from the corpus responsive to the current query taking into account at least the information from the editorial session.