公开(公告)号：US20230142768A1

公开(公告)日：2023-05-11

申请号：US17522270

申请日：2021-11-09

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Michele Saad ,  Christopher Nota

IPC: G06Q30/06 ,  G06K9/62

CPC classification number: G06Q30/0629 ,  G06Q30/0631 ,  G06K9/6215

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a recommendation model to the set of recommendable items. The recommendation model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The recommendation model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.

公开(公告)号：US11270369B2

公开(公告)日：2022-03-08

申请号：US16779074

申请日：2020-01-31

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Sridhar Mahadevan ,  Anup Bandigadi Rao

IPC: G06Q30/00 ,  G06Q30/06 ,  H04L67/50 ,  G06F16/9535

Abstract: In implementations of systems for generating recommendations, a computing device implements a recommendation system to receive prior interaction data describing prior interactions of entities with items. The recommendation system processes the prior interaction data and segments the entities into a first set and a second set. The entities included in the first set have greater numbers of prior interactions with the items than the entities included in the second set. The recommendation system then generates subset data describing a subset of the entities in the first set. This subset excludes entities having numbers of the prior interactions with the items below a threshold. The recommendation system forms a recommendation model based on the subset data and the system uses the recommendation model to generate a recommendation for display in a user interface.

公开(公告)号：US11244329B2

公开(公告)日：2022-02-08

申请号：US15413892

申请日：2017-01-24

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Trevor H. Paulsen

IPC: G06Q30/02 ,  G06Q10/10

Abstract: Metric forecasting techniques in a digital medium environment are described. A time series interval is identified by an analytics system that is exhibited by input usage data. The input usage data describes values of a metric involved in the provision of the digital content by a service provider system. A determination is then made by the analytics system as to whether historical usage data includes the identified time series interval. A forecast model is then selected by the analytics system from a plurality of forecast models based on a result of the determination and the identified time series interval. Forecast data is then generated by a forecast module of the analytics system. The forecast data is configured to predict at least one value of the metric based on the selected forecast model, a result of the determination, and the input usage data.

公开(公告)号：US20210241346A1

公开(公告)日：2021-08-05

申请号：US16779074

申请日：2020-01-31

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Sridhar Mahadevan ,  Anup Bandigadi Rao

IPC: G06Q30/06 ,  H04L29/08 ,  G06F16/9535

Abstract: In implementations of systems for generating recommendations, a computing device implements a recommendation system to receive prior interaction data describing prior interactions of entities with items. The recommendation system processes the prior interaction data and segments the entities into a first set and a second set. The entities included in the first set have greater numbers of prior interactions with the items than the entities included in the second set. The recommendation system then generates subset data describing a subset of the entities in the first set. This subset excludes entities having numbers of the prior interactions with the items below a threshold. The recommendation system forms a recommendation model based on the subset data and the system uses the recommendation model to generate a recommendation for display in a user interface.

公开(公告)号：US20200241878A1

公开(公告)日：2020-07-30

申请号：US16261092

申请日：2019-01-29

Applicant: Adobe Inc.

Inventor： Yash Chandak ,  Georgios Theocharous

IPC: G06F9/38 ,  G06F9/48 ,  G06N3/08 ,  G06N20/00

Abstract: The present disclosure relates to generating proposed digital actions in high-dimensional action spaces for client devices utilizing reinforcement learning models. For example, the disclosed systems can utilize a supervised machine learning model to train a latent representation decoder to determine proposed digital actions based on latent representations. Additionally, the disclosed systems can utilize a latent representation policy gradient model to train a state-based latent representation generation policy to generate latent representations based on the current state of client devices. Subsequently, the disclosed systems can identify the current state of a client device and a plurality of available actions, utilize the state-based latent representation generation policy to generate a latent representation based on the current state, and utilize the latent representation decoder to determine a proposed digital action from the plurality of available actions by analyzing the latent representation.

公开(公告)号：US20190295004A1

公开(公告)日：2019-09-26

申请号：US15934531

申请日：2018-03-23

Applicant: ADOBE INC.

Inventor： Sorathan Chaturapruek ,  Georgios Theocharous ,  Kent Andrew Edmonds

IPC: G06N99/00 ,  G06N5/04 ,  G06T11/60 ,  G06F9/451 ,  G06K9/62

Abstract: Systems and methods provide a recommendation system for recommending sequential content. The training of a reinforcement learning (RL) agent is bootstrapped from passive data. The RL agent of the sequential recommendations system is trained using the passive data over a number of epochs involving interactions between the sequential recommendation system and user devices. At each epoch, available active data from previous epochs is obtained, and transition probabilities are generated from the passive data and at least one parameter derived from the currently available active data. Recommended content is selected based on a current state and the generated transition probabilities, and the active data is updated from the current epoch based on the recommended content and a resulting new state. A clustering approach can also be employed when deriving parameters from active data to balance model expressiveness and data sparsity.

公开(公告)号：US20190236410A1

公开(公告)日：2019-08-01

申请号：US15886263

申请日：2018-02-01

Applicant: ADOBE INC.

Inventor： Sorathan Chaturapruek ,  Georgios Theocharous

IPC: G06K9/62 ,  G06N99/00 ,  G06N7/00

CPC classification number: G06K9/6256 ,  G06K9/6218 ,  G06N7/005 ,  G06N20/00

Abstract: Systems and methods provide for bootstrapping a sequential recommendation system from passive data. A learning agent of the sequential recommendations system is trained using the passive data over a number of epochs involving interactions between the sequential recommendation system and user devices. At each epoch, available active data from previous epochs is obtained, and transition probabilities are generated from the passive data and at least one parameter derived from the currently available active data. A recommended action is selected given a current state and the generated transition probabilities, and the active data is updated from the current epoch based on the recommended action and a resulting new state. A clustering approach can also be employed when deriving parameters from active data to balance model expressiveness and data sparsity.

公开(公告)号：US20240403651A1

公开(公告)日：2024-12-05

申请号：US18328174

申请日：2023-06-02

Applicant: Adobe Inc.

Inventor： Shripad Vilasrao Deshmukh ,  Arpan Dasgupta ,  Balaji Krishnamurthy ,  Chirag Agarwal ,  Georgios Theocharous ,  Jayakumar Subramanian

IPC: G06N3/092

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.

公开(公告)号：US12111884B2

公开(公告)日：2024-10-08

申请号：US17659983

申请日：2022-04-20

Applicant: ADOBE INC.

Inventor： Tanay Anand ,  Pinkesh Badjatiya ,  Sriyash Poddar ,  Jayakumar Subramanian ,  Georgios Theocharous ,  Balaji Krishnamurthy

IPC: G06F18/2137 ,  G06N3/088

CPC classification number: G06F18/2137 ,  G06N3/088

Abstract: Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.

公开(公告)号：US20230342425A1

公开(公告)日：2023-10-26

申请号：US17659983

申请日：2022-04-20

Applicant: ADOBE INC.

Inventor： Tanay Anand ,  Pinkesh Badjatiya ,  Sriyash Poddar ,  Jayakumar Subramanian ,  Georgios Theocharous ,  Balaji Krishnamurthy

IPC: G06K9/62 ,  G06N3/08

CPC classification number: G06K9/6251 ,  G06N3/088

Abstract: Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.