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Institution

Amazon.com

CompanySeattle, Washington, United States
About: Amazon.com is a company organization based out in Seattle, Washington, United States. It is known for research contribution in the topics: Service (business) & Service provider. The organization has 13363 authors who have published 17317 publications receiving 266589 citations.


Papers
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Patent
21 Mar 2008
TL;DR: In this article, a digital work may be annotated using an eBook reader device and an invariant location reference identifier corresponding to the specified portion of the digital work can then be added to the annotation.
Abstract: A digital work may be annotated using an eBook reader device (300). Upon receiving (706) an annotation relating to a specific portion of the digital work, an invariant location reference identifier (500) corresponding to the specified portion of the digital work may be appended (710) to the annotation. The annotation may then be stored (712) in association with the digital work for later reference. In some instances, an annotation may be presented (618) on an eBook reader device upon receipt (612) of a valid authorization credential granting access to the annotation.

183 citations

Posted Content
TL;DR: In this article, a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies is introduced, and regret analysis of this algorithm in a standard stochastic noise setting is provided.
Abstract: We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effectiveness on a number of artificial and real-world datasets. Our experiments show a significant increase in prediction performance over state-of-the-art methods for bandit problems.

183 citations

Patent
15 Dec 2005
TL;DR: In this article, a method and system for anticipatory package shipping is described, which may include packaging one or more items as a package for eventual shipment to a delivery address, selecting a destination geographical area to which to ship the package, and shipping the package to the destination geographic area without completely specifying the delivery address at time of shipment, and while the package is in transit, completely specifying delivery address for the package.
Abstract: A method and system for anticipatory package shipping are disclosed. According to one embodiment, a method may include packaging one or more items as a package for eventual shipment to a delivery address, selecting a destination geographical area to which to ship the package, shipping the package to the destination geographical area without completely specifying the delivery address at time of shipment, and while the package is in transit, completely specifying the delivery address for the package.

183 citations

Proceedings ArticleDOI
09 May 2017
TL;DR: This paper describes the architecture of Aurora and the design considerations leading to that architecture, and describes how Aurora achieves consensus on durable state across numerous storage nodes using an efficient asynchronous scheme, avoiding expensive and chatty recovery protocols.
Abstract: Amazon Aurora is a relational database service for OLTP workloads offered as part of Amazon Web Services (AWS). In this paper, we describe the architecture of Aurora and the design considerations leading to that architecture. We believe the central constraint in high throughput data processing has moved from compute and storage to the network. Aurora brings a novel architecture to the relational database to address this constraint, most notably by pushing redo processing to a multi-tenant scale-out storage service, purpose-built for Aurora. We describe how doing so not only reduces network traffic, but also allows for fast crash recovery, failovers to replicas without loss of data, and fault-tolerant, self-healing storage. We then describe how Aurora achieves consensus on durable state across numerous storage nodes using an efficient asynchronous scheme, avoiding expensive and chatty recovery protocols. Finally, having operated Aurora as a production service for over 18 months, we share the lessons we have learnt from our customers on what modern cloud applications expect from databases.

182 citations

Patent
Francis J. Kane1, Cory Hicks1
28 Dec 2007
TL;DR: In this paper, a content provider system interacts with a network of web sites to provide behavior-based content to users by adding widgets to selected web pages of their sites, when executed on the computing devices of users who view the selected Web pages, report user-generated events to the content provider.
Abstract: A content provider system interacts with a network of web sites to provide behavior-based content to users. Operators of the web sites add widgets to selected web pages of their sites. The widgets, when executed on the computing devices of users who view the selected web pages, report user-generated events to the content provider system. The content provider system analyzes the reported events to detect behavioral associations between particular web sites, web pages, products, and/or other types of items. The widgets may also retrieve and display behavior-based content that is based on these item-to-item behavioral associations. For example, when a user views a particular web page, a widget on that page may request and display descriptions of, and links to, other sites or pages that are (a) behaviorally related to the page being viewed or an item represented thereon, and/or (b) behaviorally related to the past browsing activities of the particular user.

181 citations


Authors

Showing all 13498 results

NameH-indexPapersCitations
Jiawei Han1681233143427
Bernhard Schölkopf1481092149492
Christos Faloutsos12778977746
Alexander J. Smola122434110222
Rama Chellappa120103162865
William F. Laurance11847056464
Andrew McCallum11347278240
Michael J. Black11242951810
David Heckerman10948362668
Larry S. Davis10769349714
Chris M. Wood10279543076
Pietro Perona10241494870
Guido W. Imbens9735264430
W. Bruce Croft9742639918
Chunhua Shen9368137468
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20234
2022168
20212,015
20202,596
20192,002
20181,189