Amazon.com

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.

Method and system for aggregating and presenting user highlighting of content

Abstract: Highlighting of content is aggregated across a plurality of users, thus enabling the content to be presented with highlights that represent the collective highlighting of the users. Highlighted content may be presented to the users with varying levels of prominence. Accordingly, depending on the aggregated highlight information, some content may be presented with a highlight that is more or less prominent than highlighting for other content. Prominence data associated with highlighted content may include a score indicative of a strength of the highlight for the highlighted content. A score indicating a stronger highlight causes the highlight to be presented with greater prominence. The score may be incremented, possibly on a weighted bases, to represent the combined highlighting of different users. Highlights may assume many different forms, including visual forms (such as colors, lines, borders, fonts, icons, etc.), audio forms and tactile forms.

Cluster-based management of collections of items

Abstract: Computer-implemented processes are disclosed for clustering items and improving the utility of item recommendations. One process involves applying a clustering algorithm to a user's collection of items. Information about the resulting clusters is then used to select items to use as recommendation sources. Another process involves displaying the clusters of items to the user via a collection management interface that enables the user to attach cluster-level metadata, such as by rating or tagging entire clusters of items. The resulting metadata may be used to improve the recommendations generated by a recommendation engine. Another process involves forming clusters of items in which a user has indicated a lack of interest, and using these clusters to filter the output of a recommendation engine. Yet another process involves applying a clustering algorithm to the output of a recommendation engine to arrange the recommended items into cluster-based categories for presentation to the user.

Managing virtual machine migration

Abstract: Systems and method for the management of migrations of virtual machine instances are provided. A migration manager monitors the resource usage of a virtual machine instance over time in order to create a migration profile. When migration of a virtual machine instance is desired, the migration manager schedules the migration to occur such that the migration conforms to the migration profile.

Storage object deletion job management

Abstract: Methods and apparatus for storage object deletion job management are disclosed. A storage medium stores program instructions that when executed on a processor, identify, during a particular deletion execution iteration, a particular deletion job object stored at a multi-tenant storage service, wherein the particular deletion job object indicates a collection of storage objects that are eligible for deletion from the storage service in accordance with specified deletion criteria. The instructions determine, based on a job validity criterion, whether deletion operations corresponding to the particular deletion job object of the one or more deletion job objects are to be scheduled. If the job object is validated, the instructions initiate a deletion operation for storage objects indicated in the particular deletion job object.

Fixed-budget kernel recursive least-squares

Abstract: We present a kernel-based recursive least-squares (KRLS) algorithm on a fixed memory budget, capable of recursively learning a nonlinear mapping and tracking changes over time. In order to deal with the growing support inherent to online kernel methods, the proposed method uses a combined strategy of growing and pruning the support. In contrast to a previous sliding-window based technique, the presented algorithm does not prune the oldest data point in every time instant but it instead aims to prune the least significant data point. We also introduce a label update procedure to equip the algorithm with tracking capability. Simulations show that the proposed method obtains better performance than state-of-the-art kernel adaptive filtering techniques given similar memory requirements.