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.

Keyword determinations from voice data

Abstract: Topics of potential interest to a user, useful for purposes such as targeted advertising and product recommendations, can be extracted from voice content produced by a user. A computing device can capture voice content, such as when a user speaks into or near the device. One or more sniffer algorithms or processes can attempt to identify trigger words in the voice content, which can indicate a level of interest of the user. For each identified potential trigger word, the device can capture adjacent audio that can be analyzed, on the device or remotely, to attempt to determine one or more keywords associated with that trigger word. The identified keywords can be stored and/or transmitted to an appropriate location accessible to entities such as advertisers or content providers who can use the keywords to attempt to select or customize content that is likely relevant to the user.

Updating routing information based on client location

Abstract: A system, method, and computer-readable medium for updating request routing information associated with client location information are provided. A content delivery network service provider receives a DNS query from a client computing device. The DNS query corresponds to a resource identifier for requested content from the client computing device. The content delivery network service provider obtains a query IP address corresponding to the client computing device. Based on routing information associated with the query IP address, the content delivery network service provider routes the DNS query. The process further includes monitoring performance data associated with the transmission of the requested resource and updating routing information associated with the query IP address based on the performance data for use in processing subsequent requests form the client computing device.

Utilizing phrase tokens in transactions

Abstract: A system and method for facilitating transactions utilizing phrase tokens are provided. Individual entities can be associated with unambiguous transaction phrase tokens, such as multiple word phrases. The transaction phrase tokens are associated with transaction accounts by a service provider such that the entities can complete a transaction without having to exchange transaction account information. In a transaction, a transaction phrase token is offered to an accepting party, which tenders the offered transaction phrase token to the service provider. The service provider processes the offered transaction phrase token according to configuration information specified for the transaction phrase token. The service provider can automatically process the transaction request or request additional information.

Task2Vec: Task Embedding for Meta-Learning

Abstract: We introduce a method to generate vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a dataset with ground-truth labels and a loss function, we process images through a "probe network" and compute an embedding based on estimates of the Fisher information matrix associated with the probe network parameters. This provides a fixed-dimensional embedding of the task that is independent of details such as the number of classes and requires no understanding of the class label semantics. We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks. We demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a novel task. We present a simple meta-learning framework for learning a metric on embeddings that is capable of predicting which feature extractors will perform well on which task. Selecting a feature extractor with task embedding yields performance close to the best available feature extractor, with substantially less computational effort than exhaustively training and evaluating all available models.

Lessons and Challenges from Mining Retail E-Commerce Data

Abstract: The architecture of Blue Martini Software's e-commerce suite has supported data collection, data transformation, and data mining since its inception. With clickstreams being collected at the application-server layer, high-level events being logged, and data automatically transformed into a data warehouse using meta-data, common problems plaguing data mining using weblogs (e.g., sessionization and conflating multi-sourced data) were obviated, thus allowing us to concentrate on actual data mining goals. The paper briefly reviews the architecture and discusses many lessons learned over the last four years and the challenges that still need to be addressed. The lessons and challenges are presented across two dimensions: business-level vs. technical, and throughout the data mining lifecycle stages of data collection, data warehouse construction, business intelligence, and deployment. The lessons and challenges are also widely applicable to data mining domains outside retail e-commerce.