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.

Densest subgraph in streaming and MapReduce

Abstract: The problem of finding locally dense components of a graph is an important primitive in data analysis, with wide-ranging applications from community mining to spam detection and the discovery of biological network modules. In this paper we present new algorithms for finding the densest subgraph in the streaming model. For any e > 0, our algorithms make O(log1+en) passes over the input and find a subgraph whose density is guaranteed to be within a factor 2(1 + e) of the optimum. Our algorithms are also easily parallelizable and we illustrate this by realizing them in the MapReduce model. In addition we perform extensive experimental evaluation on massive real-world graphs showing the performance and scalability of our algorithms in practice.

Genetic Introgression: An Integral but Neglected Component of Speciation in Birds

Abstract: The Auk, Vol. 128, Number 4, pages 620−632. ISSN 0004-8038, electronic ISSN 1938-4254.  2011 by The American Ornithologists’ Union. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press’s Rights and Permissions website, http://www.ucpressjournals. com/reprintInfo.asp. DOI: 10.1525/auk.2011.128.4.620 3E-mail: frankrheindt@yahoo.com.au Hybridization, or the act of reproduction between different species, is common in birds (Grant and Grant 1992) and can be an agent of adaptive evolution (Veen et al. 2001). When hybridization occurs, a species’ genetic material may enter the gene pool of another and thereby introduce genetic novelty. This process is called “genetic introgression” (hereafter “introgression”; Anderson 1949). Although known for decades, the incidence of introgression in nature has long been underappreciated (e.g., Currat et al. 2008). Only recently have evolutionists come to regard it as an important and pervasive mechanism in speciation, in the maintenance of genetic diversity and in the introduction of advantageous novelty into the gene pool (Arnold et al. 1999, Noor et al. 2000, Seehausen 2004). In fact, genetic data now suggest that our own species may have undergone a complicated history of introgression with at least two extinct hominine lineages (Green et al. 2010, Reich et al. 2010). Another reason why introgression is an important topic is the impact of human-induced climate change, which is rapidly shifting vegetation boundaries and creating new hybrid zones, with little-known consequences for the genetic integrity of species (Mank et al. 2004, Brumfield 2010). There has been a tremendous increase in introgressionrelated avian research since a comprehensive overview of the introgression complex between the Blue-winged Warbler (Vermivora cyanoptera) and the Golden-winged Warbler (V. chrysoptera) appeared in The Auk five years ago (Confer 2006). Here, we seek to fill a gap by summarizing the most important recent advances in avian introgression research and our current understanding of locus-specific differences in introgression dynamics, the detection of introgression, its directionality, and its relevance to conservation, phylogenetics, and speciation research.

User-generated activity maps

Abstract: Apparatus and computer-readable media for associating metadata with a geographic location are provided. The apparatus includes logic for detecting that a mobile device is present at a geographic location relevant to a user of the mobile device, logic for retrieving context information associated with the location, logic for selecting a program code module based upon a contextual relevancy of the location, logic for providing the program code module for execution, where the program code module is capable of performing processing specific to at least one aspect of the location, the processing is based upon the context information, and the program code module is further capable of receiving at least one input data item from the mobile device, where the at least one input data item describes an activity of the user at the location, and logic for associating the at least one input data item with the location.

Beyond clicks: dwell time for personalization

Abstract: Many internet companies, such as Yahoo, Facebook, Google and Twitter, rely on content recommendation systems to deliver the most relevant content items to individual users through personalization. Delivering such personalized user experiences is believed to increase the long term engagement of users. While there has been a lot of progress in designing effective personalized recommender systems, by exploiting user interests and historical interaction data through implicit (item click) or explicit (item rating) feedback, directly optimizing for users' satisfaction with the system remains challenging. In this paper, we explore the idea of using item-level dwell time as a proxy to quantify how likely a content item is relevant to a particular user. We describe a novel method to compute accurate dwell time based on client-side and server-side logging and demonstrate how to normalize dwell time across different devices and contexts. In addition, we describe our experiments in incorporating dwell time into state-of-the-art learning to rank techniques and collaborative filtering models that obtain competitive performances in both offline and online settings.

Aggregating crowdsourced binary ratings

Abstract: In this paper we analyze a crowdsourcing system consisting of a set of users and a set of binary choice questions. Each user has an unknown, fixed, reliability that determines the user's error rate in answering questions. The problem is to determine the truth values of the questions solely based on the user answers. Although this problem has been studied extensively, theoretical error bounds have been shown only for restricted settings: when the graph between users and questions is either random or complete. In this paper we consider a general setting of the problem where the user--question graph can be arbitrary. We obtain bounds on the error rate of our algorithm and show it is governed by the expansion of the graph. We demonstrate, using several synthetic and real datasets, that our algorithm outperforms the state of the art.