Samsung Electronics Co., Ltd.

Hong-Hoe Kim

Yingnan Zhu

Xiangyuan Zhao

Tomasz Jan Palczewski

Hari Babu Nayar

Chaitanya Praveen Pratury

Anirudh Rao

Hyun Chul Lee

Peng Zhou

A method includes obtaining, based on a sequential graph-based model, gaming exposure information over time, where the gaming exposure information includes device-level preferences and household-level preferences. The method also includes combining one or more raw user behavior sessions into a gameplay session based on the obtained gaming exposure information. The method further includes providing a scoring metric to (i) check an extent of multi-matching in the obtained gaming exposure information and (ii) remove untrustworthy gaming exposures from the obtained gaming exposure information. In addition, the method includes generating, based on a feature engineering pipeline, one or more game segments running in a production environment, where the one or more game segments are identified for ancillary content based on inferences by a machine learning model trained using the gaming exposure information.

Effective multi-scale multi-granular targeting for game users

A method includes providing a Fibonacci confidence interval level on user program viewership data to distinguish user interest level in different linear entertainment programs. The method also includes creating a behavior shift feature space for acquiring information about user behavior over time based on a behavior sequence, a first derivative on the behavior sequence, and a second derivative on the behavior sequence. The method further includes utilizing, based on a trained machine learning model, a transformer structure and attention to map user data from the behavior shift feature space into a prediction of the Fibonacci confidence interval level.

Behavior shift-based interest level prediction

In one embodiment, a method includes receiving user behavior data and contextual information associated with the user behavior data, the contextual information including a first data portion associated with a first context type. The method includes generating, from the user behavior data and the contextual information using a hashing algorithm, a first heterogeneous hash code including a first portion representing the user behavior data and a second hash code portion representing the first data portion associated with the first context type. The method includes accessing a second heterogeneous hash code including a third hash code portion representing a second data portion associated with the first context type. The method includes comparing the first heterogeneous hash code with the second heterogeneous hash code including determining similarity between the second hash code portion of the first heterogeneous hash code and the third hash code portion of the second heterogenous hash code.

Hashing-based effective user modeling

A method includes obtaining (i) device data associated with multiple electronic devices and (ii) advertisement data associated with one or more advertisement campaigns. The method also includes identifying first features and second features, where the first features correspond to the device data and the second features correspond to the advertisement data. The method further includes generating a graph relating usage history of the multiple electronic devices and one or more advertisement genres. The method also includes identifying a specified electronic device from among the multiple electronic devices and an advertisement segment from among the one or more advertisement campaigns using the first features, the second features, and the graph. In addition, the method includes providing the advertisement segment to the specified electronic device.

Growing community of inventors

Aliso Viejo, CA, United States of America

Hong-Hoe Kim