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.

System and method for providing advertisement based on mobile device travel patterns

Abstract: Mobile device users may be tracked either via mobile-signal triangulation or via Global Positioning Satellite information. A mobile device user's recent movements may be analyzed to determine trails or traffic patterns for device user among various locations. Mobile device trail information, either for an individual user or aggregated for multiple users, may be analyzed to determine a next destination for the user. Electronic advertising content, such as advertisements, coupons and/or other communications, associated with the next destination may be sent to an electronic device likely to be viewed by the mobile device user. Additionally, the identity of the mobile device user may be known and the advertisements or coupons may be tailored according to demographic information regarding the mobile device user. In addition, destinations may be recommended to mobile device users based on the recent locations the users have visited.

Forecasting: theory and practice

Abstract: Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.

Method and system for conducting a discussion relating to an item

Abstract: A method and system for conducting an electronic discussion relating to a topic. The discussion system of the present invention receives a selection of an item that is to be the topic of the discussion. The discussion system then receives comments relating to the selected item and generates a message that includes a description of the selected item and the received comments. The discussion system then sends the generated message to participants of the discussion. The discussion system receives from a participant who received the generated message additional comments that are to be added to the generated message. The discussion system sends the generated message along with received additional comments to the participants of the discussion.

Synchronizing the playing and displaying of digital content

Abstract: The techniques disclosed herein allow a user to synchronize the playing and displaying of digital content on an electronic device. The device may render a first portion of digital content so it may be displayed. The device may also play a segment of the digital content as audio using text to speech software. The device may also render a second portion of digital content for display depending on whether the position of the last word read is greater than the last position in the first portion of digital content.

Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation

Abstract: The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithms remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.