Amazon Technologies, Inc.

Leon Portman

Liane Lewin-Eytan

Zachary Thomas Crowell

Ramakanthachary S Gottumukkala

Ran Levy

Mehul Jain

Gary Weiss

Ido Arad

Hadar Neumann

Stanislav Klyachkovsky

Eyal Itah

Ori Rozen

Yotam Yarden

Knaan Ratosh

Systems, methods, and computer-readable media are disclosed for determining a synthetic audible utterance regarding a product, processing the audible utterance via a catalog service to determine a result and determining the relevance of the result and/or success of the catalog service for benchmarking, marketing, and catalog service improvement purposes. The audible utterance may be based on the product and/or product information as well as an intended action to be performed by the catalog service corresponding to the synthetic utterance. A relevance score may be generated using an embedding algorithm to determine the accuracy and/or appropriateness of the action performed by the catalog service.

Systems and methods for generation, execution and evaluation of synthetic shopping sessions

Technologies are provided for generation of recommendation results using a verbal query. In one embodiment, query data can be generated using a searchable query corresponding to the verbal query. The query data can define a query browse node and a product brand, for example. First product identifiers that match the searchable query can be determined using multiple data repositories. Duplicates from the first product identifiers can be removed, resulting in second product identifiers. Attribute data also can be generated using the second product identifiers. The attribute data can define features for a product identifier. Further, third product identifiers can be determined by applying a filtering model to the second product identifiers. A ranking of the third product identifiers can be generated using an optimization function based on the query data and the product attribute data. A product corresponding to one of the ranked product identifiers represents a recommendation result.

Generation of recommendation results using a verbal query

Techniques for natural language searches are described. In an example, a computer system receives a search associated with a computing device. Based on the search, the computer system determines first search results from a first data source and associated with a first type and second search results from a second data source and associated with a type. The computer system also determines a context associated with at least one of the computing device or a user. Based on the context, the computer system generates instructions associated with a presentation of the first search results and the second search results at a user interface of the computing device. The instructions indicate a first presentation order of the first search results and the second search results and a second presentation order of search results within the first search results. The computer system sends the instructions to the computing device.

Context-based natural language searches

Systems and methods are described herein for generating an adaptive response to a user request. Input indicative of a user request may be received and utilized to identify an item in an electronic catalog. Title segments may be identified from the item's title. Significant segments of the user request may be determined. In response to the user request, a shortened title may be generated from the identified title segments and provided as output at the user device (e.g., via audible output provided at a speaker of the user device, via textual output, or the like). At least one of the title segments provided in the shortened title may correlate to the significant segment identified from the user request. In some embodiments, the length and content of the shortened title may vary based at least in part on the contextual intent of the user's request.

Techniques for providing adaptive responses

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Givatayim, Israel

Leon Portman