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.

Learning from Logged Implicit Exploration Data

Abstract: We provide a sound and consistent foundation for the use of nonrandom exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chosen action is learned. The primary challenge in a variety of settings is that the exploration policy, in which "offline" data is logged, is not explicitly known. Prior solutions here require either control of the actions during the learning process, recorded random exploration, or actions chosen obliviously in a repeated manner. The techniques reported here lift these restrictions, allowing the learning of a policy for choosing actions given features from historical data where no randomization occurred or was logged. We empirically verify our solution on two reasonably sized sets of real-world data obtained from Yahoo!.

Dynamics of conversations

Abstract: How do online conversations build? Is there a common model that human communication follows? In this work we explore these questions in detail. We analyze the structure of conversations in three different social datasets, namely, Usenet groups, Yahoo! Groups, and Twitter. We propose a simple mathematical model for the generation of basic conversation structures and then refine this model to take into account the identities of each member of the conversation.

Anonymizing moving objects: how to hide a MOB in a crowd?

Abstract: Moving object databases (MOD) have gained much interest in recent years due to the advances in mobile communications and positioning technologies. Study of MOD can reveal useful information (e.g., traffic patterns and congestion trends) that can be used in applications for the common benefit. In order to mine and/or analyze the data, MOD must be published, which can pose a threat to the location privacy of a user. Indeed, based on prior knowledge of a user's location at several time points, an attacker can potentially associate that user to a specific moving object (MOB) in the published database and learn her position information at other time points.In this paper, we study the problem of privacy-preserving publishing of moving object database. Unlike in microdata, we argue that in MOD, there does not exist a fixed set of quasi-identifier (QID) attributes for all the MOBs. Consequently the anonymization groups of MOBs (i.e., the sets of other MOBs within which to hide) may not be disjoint. Thus, there may exist MOBs that can be identified explicitly by combining different anonymization groups. We illustrate the pitfalls of simple adaptations of classical k-anonymity and develop a notion which we prove is robust against privacy attacks. We propose two approaches, namely extreme-union and symmetric anonymization, to build anonymization groups that provably satisfy our proposed k-anonymity requirement, as well as yield low information loss. We ran an extensive set of experiments on large real-world and synthetic datasets of vehicular traffic. Our results demonstrate the effectiveness of our approach.

Matching and ranking of sponsored search listings incorporating web search technology and web content

Abstract: A system is disclosed for generating a search result list in response to a search request from a searcher using a computer network. A first database is maintained that includes a first plurality of search listings. A second database is maintained that includes documents having general web content. A search request is received from the searcher. A first set of search listings is identified from the first database having documents generating a match with the search request and a second set of search listings is identified from the second database having documents generating a match with the search request. A confidence score is determined for each listing from the first set of search listings wherein the confidence score is determined in accordance with a relevance of each listing when compared to the listings of the second set of search listings. The identified search listings from the first set of search listing are ordered in accordance, at least in part, with the confidence score for each search listing.

Effects of the Molecular Weight and the Degree of Deacetylation of Chitosan Oligosaccharides on Antitumor Activity

Abstract: Effects of the degree of deacetylation (DDA) and the molecular mass of chitosan oligosaccharides (CTS-OS), obtained from the enzymatic hydrolysis of high molecular weight chitosan (HMWC), on antitumor activity was explored. The DDA and molecular weights of CTS-OS were determined by matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-TOF MS) analysis. The CTS-OS were found to be a mixture of mainly dimers (18.8%), trimers (24.8%), tetramers (24.9%), pentamers (17.7%), hexamers (7.1%), heptamers (3.3%), and octamers (3.4%). The CTS-OS were further fractionated by gel-filtration chromatography into two major fractions: (1) COS, consisting of glucosamine (GlcN)(n), n = 3-5 with DDA 100%; and (2) HOS, consisting of (GlcN)(5) as the minimum residues and varying number of N-acetylglucosamine (GlcNAc)(n), n = 1-2 with DDA about 87.5% in random order. The cytotoxicities, expressed as the concentration needed for 50% cell death (CC(50)), of CTS-OS, COS, and HOS against PC3 (prostate cancer cell), A549 (lung cancer cell), and HepG2 (hepatoma cell), were determined to be 25 μg·mL(-1), 25 μg·mL(-1), and 50 μg·mL(-1), respectively. The HMWC was approximately 50% less effective than both CTS-OS and COS. These results demonstrate that the molecular weight and DDA of chitosan oligosaccharides are important factors for suppressing cancer cell growth.