公开(公告)号：US20250036706A1

公开(公告)日：2025-01-30

申请号：US18226079

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Yuan Yuan ,  Bei Huang ,  Lijing Wang ,  Yancheng Li ,  Bowen Wang ,  Jin Xu ,  Zhenyu Yan ,  Qilong Yuan

IPC: G06F16/957 ,  G06F9/451 ,  H04L67/50

Abstract: In some embodiments, a computing system extrapolates aggregated interaction data associated with users of an online platform by applying a mixed granularity model to generate extrapolated interaction data for each user in the users. The aggregated interaction data includes a total number of occurrences of a target action performed by the users with respect to the online platform. The extrapolated data includes a series of actions leading to the target action for each user. The computing system identifies an impact of each action in the series of actions for each user on leading to the target action based, at least in part, upon the extrapolating a series of actions associated with the user. User interfaces presented on the online platform can be modified based on at least the identified impacts to improve customization of the user interfaces to the users or enhance an experience of the users.

公开(公告)号：US20240311643A1

公开(公告)日：2024-09-19

申请号：US18185828

申请日：2023-03-17

Applicant: Adobe Inc.

Inventor： Bowen Wang ,  Yuan Yuan ,  Bei Huang ,  Lijing Wang ,  Yancheng Li ,  Jin Xu ,  Qilong Yuan ,  Zhenyu Yan

IPC: G06N3/096 ,  G06N3/04

CPC classification number: G06N3/096 ,  G06N3/04

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified analytics prediction machine learning model using an iterative transfer learning approach. For example, the disclosed systems generate an initial version of an analytics prediction machine learning model for predicting an analytics metric according to learned parameters. In some embodiments, the disclosed systems determine expected data channel contributions for the analytics metric according to prior data. Additionally, in some cases, the disclosed systems generate a modified analytics prediction machine learning model by iteratively updating model parameters such that predicted data channel contributions are within a threshold similarity of expected data channel contributions.