Invention Grant
- Patent Title: Generating a predictive behavior model for predicting user behavior using unsupervised feature learning and a recurrent neural network
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Application No.: US15812568Application Date: 2017-11-14
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Publication No.: US10990889B2Publication Date: 2021-04-27
- Inventor: Bo Peng , Julia Viladomat , Zhenyu Yan , Abhishek Pani
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Kilpatrick Townsend & Stockton LLP
- Main IPC: G06N7/00
- IPC: G06N7/00 ; G06N3/08 ; G06N3/04

Abstract:
Certain embodiments involve a model for predicting user behavior. For example, a system accesses user behavior data indicating various users' behaviors during intervals over various periods of time and target behavior data indicating a particular user behavior. The system associates each user with a label that indicates whether a user performed a particular action during or after a time period based on the target behavior data. The system uses the user behavior data to train various deep Restricted Boltzmann Machines (“RBM”) to generate representations of each user over each period of time that indicate the user behavior over the time period. The system generates a predictive model by connecting the RBMs into a deep recurrent neural network and uses the target behavior data associated with each user, along with the representations of each user, as input data to train the deep recurrent neural network to predict user behavior.
Public/Granted literature
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06N | 基于特定计算模型的计算机系统 |
G06N7/00 | 基于特定数学模式的计算机系统 |