Amazon Technologies, Inc.

Tian Chen

Feng Zhu

Calvin Yue-Ren Kuo

Dan Catalin Teodorescu

Zheshen Wang

Stuart Olmstead-Wilcox

Siwei Jia

Chuanhai Zhang

Jiang Du

Mohammad Pirhooshyaran

Ashish Satish Urankar

Jonathan Toner

Zhaofeng Zhan

Taeuk Jang

Chunlei Zhang

Jingyu Dong

Hansu Gu

Methods, systems, and computer-readable media for user engagement modeling for engagement optimization are disclosed. Based (at least in part) on one or more user engagement models, a user engagement modeling system determines an uplift score for a user of an Internet-accessible service. The uplift score comprises an estimated effect on one or more user engagement metrics of an incentive to interact with the service. The uplift score is determined based (at least in part) on values of the user engagement metric(s) for the user in a treatment group and values of the metric(s) for the user in a control group, in view of propensity score to be in either group. The treatment group is offered the incentive, and the control group is not offered the incentive. The system determines that the user is or is not offered the incentive based at least in part on the uplift score.

User engagement modeling for engagement optimization

Systems and methods for intelligent defaulting using machine learning are provided. The systems and methods may involve the use of one or more machine learning models that are used to determine a default shipment option to present to a user through a user interface associated with a product checkout page (for example, on a website or through a mobile device application). The determination of the default shipment option may be performed in two phases. A first phase may involve using user-specific historical data (for example, prior selected shipment options, order information, etc.) to determine scores associated with individual potential shipment options. The scores may indicate a probability that the user may select the individual shipment options. The individual shipment options may also be provided associated threshold values. A second phase may involve adjusting the thresholds based on non-user-specific factors, such as delivery driver preference, profits and other financial considerations, and other factors. A final shipment option may be selected by determining the highest probability score that satisfies its associated threshold. The default shipment option may be presented to the user by default when the user reached the checkout page for the product.

Systems and methods for intelligent defaulting using machine learning

Systems and methods are provided for detecting discrepancies in information that is displayed to a user across different webpages of an online shopping system. User interaction with the online shopping system is simulated in one example to collect data relating to webpages of the online shopping system. The data is analyzed to identify relevant semantic blocks of data and compare the data in those blocks across webpages. In an additional example, source code for the webpages is analyzed using machine-learning systems, which are targeted based on the results of the user interaction simulation to identify problematic source code and correct associated issues.

Detecting and correcting user interface discrepancies

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Redmond, WA, United States of America

Tian Chen