Amazon Technologies, Inc.

V Divya Bhargavi

Vidya Sagar Ravipati

Xiaohan Nie

Rishita Rajal Anubhai

Ahmed Aly Saad Ahmed

Yash Chaturvedi

Karan Sindwani

Liwen You

David Kuo

Siavash Gholami

Systems and techniques are disclosed for determining relevant search query autocompletions for presentation to a user that has entered a prefix into a search application interface. A target search space represented as a knowledge graph may be searched to generate a subgraph of nodes representing autocompletion candidates that correspond to the prefix. Further historical and/or interaction data associated with the user and/or other users of the search application may be used to detemir additional autocompletion candidates. A machine-learned model may be trained to score and rank the candidates based on embeddings extracted by the model for the autocompletion candidates. A listing of the autocompletion candidate ordered based on the scores may be presented as autocompletion suggestions to the user.

Enhanced search autocompletion framework

Techniques for a computer-implemented service for virtual product placement in video frames are described. According to some embodiments, a computer-implemented method includes receiving, at a virtual product placement service, a request to place a two-dimensional image of a virtual product into a video, identifying, by a machine learning model of the virtual product placement service, a surface depicted in the video for insertion of the two-dimensional image of the virtual product, inserting, by the virtual product placement service, of the two-dimensional image of the virtual product into one or more frames of the video onto the surface to generate a video including the virtual product, and transmitting the video including the virtual product to a viewer device or a storage location.

Computer-implemented methods of an automated framework for virtual product placement in video frames

Techniques for aligning (merging) knowledge graphs is described. Graph entity alignment is the problem of 'joining' two knowledge graphs based on common entities. Most approaches in literature solve this using some annotated seed entity pairs and train a supervised model to rank entities in one knowledge base against another. In some examples, unsupervised alignment approach that uses graph-to-text summaries to encode entities in two or more distinct graphs into the same representation space, encodes those summaries into a common space, and then uses similarity analysis to determine when graph entities should be aligned (merged).

Knowledge graph alignment

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Atlanta, GA, United States of America

V Divya Bhargavi