Amazon Technologies, Inc.

Yahoo Assets LLC

Verizon Media Inc.

Shaunak Mishra

Anirban Sengupta

Maxim I Sviridenko

Lakshmi Narayan Bhamidipati

Ravendar Lal

Aasish Kumar Pappu

Yifan Hu

Srinath Ravindran

Kevin Yen

Changwei Hu

Mahmoudreza Abasi

Chao-Hung Wang

Vishal Ramesh Raithatha

Richard Hin-Fai Tang

Gaurav Srivastava

Manisha Verma

Yichao Zhou

Avinash Chukka

Yu Tian

Max Edward Beech

Kamil Michal Zasadzinski

Wei Yu Lin

Hua-Ying Tsai

Chaoran Wei

Xidong Pi

Phani Teja Anumanchupallik

Sarat Chandra Vadapalli

Mikhail Kuznetsov

Techniques for providing information for presentation at a device are described. In an example, a computer system receives, from a device, a query indicating that item information is requested. The computer system determines a topic for the item information. The computer system determines a sub-topic of the topic, the sub-topic generated based at least in part on a semantic overlap between predefined item attribute refinements and user contributed entries related to the item. The computer system causes the device to present the item information, the topic, and the sub-topic in association with the topic.

Topics and sub-topics for presenting search results on a device

Systems and methods for trending product recommendations are provided herein. Particularly, click-through-rate (CTR) and conversion-rate (CVR) data metrics associated with various products may be analyzed to identify trending products. Keyphrases may then be extracted from information associated with these trending products and provided to an unsupervised graph-based model to determine a relative importance of each keyphrase relative to other keyphrases extracted from information associated with other trending products. Keyphrases that are determined to be the most 'important' may then be presented along with the trending products in a trending product widget when a user searches for products.

Systems and methods for trending product recommendations

In an example, a first set of text may be received from a client device. A set of content items may be selected from among content items based upon the first set of text and a plurality of sets of content item text associated with the content items. A set of terms may be determined based upon the first set of text and the set of content items. A similarity profile associated with the set of terms may be generated. The similarity profile is indicative of similarity scores associated with similarities between terms of the set of terms. Relevance scores associated with the set of terms may be determined based upon the similarity profile. One or more search terms may be selected from among the set of terms based upon the relevance scores. A search may be performed based upon the one or more search terms.

Search query generation based upon received text

Disclosed are various embodiments for inferring brand similarities using graph neural networks and selection prediction. In one embodiment, a brand-to-brand graph is generated indicating similarities between a set of brands according to at least one of: click-through data or conversion data. Using a first graph neural network (GNN) tower, the brand-to-brand graph is analyzed to determine brand similarities among a first brand identified from a search query and a first set of other brands. Using a second GNN tower, the brand-to-brand graph is analyzed to determine brand similarities among a second brand and a second set of other brands. A level of similarity between the first brand and the second brand is determined based at least in part on an output of the first GNN tower and an output of the second GNN tower.

Inferring brand similarities using graph neural networks and selection prediction

One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

Performance metric prediction and content item text suggestion based upon content item text

One or more computing devices, systems, and/or methods are provided. An informational database may be analyzed based upon an entity to identify an informational article. The informational article may be analyzed to identify one or more first references associated with a first sentiment category and/or one or more second references associated with a second sentiment category. Sentiment tags, indicative of sentiment categories, may be assigned to one or more first reference content items associated with the one or more first references and/or to one or more second reference content items associated with the one or more second references. For each content item of a plurality of content items associated with the entity, a sentiment category associated with the content item may be determined based upon a comparison of the content item with the one or more first reference content items and the one or more second reference content items.

Determining sentiment of content and selecting content items for transmission to devices

One or more computing devices, systems, and/or methods for selecting content for transmission to devices are provided. First activity performed using a content interface on a device may be detected. First activity information associated with the first activity may be stored in an activity profile associated with the device. An activity database may be analyzed based upon the activity profile to identify one or more activity identifiers of the activity database. The activity database may comprise a plurality of activity identifiers and/or funnel stage tags associated with the plurality of activity identifiers. A request for content, associated with an entity, may be received from the device. A funnel stage score may be determined based upon one or more funnel stage tags associated with the entity and/or the one or more activity identifiers. A content item, associated with the entity, may be selected based upon the funnel stage score.

Stage-based content item selection and transmission

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Jersey City, NJ, United States of America

Shaunak Mishra