公开(公告)号：US11038976B2

公开(公告)日：2021-06-15

申请号：US16564768

申请日：2019-09-09

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: H04L29/08 ,  G06N3/08 ,  G06N3/04 ,  G06F16/2457

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

公开(公告)号：US10990889B2

公开(公告)日：2021-04-27

申请号：US15812568

申请日：2017-11-14

Applicant: Adobe Inc.

Inventor： Bo Peng ,  Julia Viladomat ,  Zhenyu Yan ,  Abhishek Pani

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.

公开(公告)号：US20200027103A1

公开(公告)日：2020-01-23

申请号：US16042882

申请日：2018-07-23

Applicant: Adobe Inc.

Inventor： Jin Xu ,  Zhenyu Yan ,  Wenqing Yang ,  Tianyu Wang ,  Abhishek Pani

IPC: G06Q30/02 ,  G06Q10/06 ,  G06N7/00

Abstract: Prioritization techniques and systems are described that utilize a historical purchase sequence and customer features to prioritize products and services to generate product and service recommendations. In an example, feature data describing a customer and historical purchase data for the customer is received that indicates products or services purchased by the customer. The historical purchase data further includes indicators of when the products or services were purchased by the customer. Then, probabilities of future purchases by the customer of additional products are determined by classifying the additional products using a multiclass classification. The multiclass classification is based on the historical purchase data and the feature data describing the customer. Next, a ranking of the additional products is generated based on the determined probabilities of future purchases. The ranking of the additional products is output in a user interface based on the determined probabilities.

公开(公告)号：US20190303980A1

公开(公告)日：2019-10-03

申请号：US15938449

申请日：2018-03-28

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Chen Dong ,  Abhishek Pani ,  Yuan Yuan

IPC: G06Q30/02 ,  G06N99/00

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that train and utilize multi-phase learning models to predict performance during digital content campaigns and provide digital content to client devices in a real-time bidding environment. In particular, one or more embodiments leverage organizational structure of digital content campaigns to train two learning models, utilizing different data sources, to predict performance, generate bid responses, and provide digital content to client devices. For example, the disclosed systems can train a first performance learning model in an offline mode utilizing parent-level historical data. Then, in an online mode, the disclosed systems can train a second performance learning model utilizing child-level historical data and utilize the first performance learning model and the second performance learning model to generate bid responses and bid amounts in a real-time bidding environment.