公开(公告)号：US20240137394A1

公开(公告)日：2024-04-25

申请号：US18049360

申请日：2022-10-24

Applicant: Spotify AB

Inventor： Joseph Cauteruccio ,  Mehdi Ben Ayed ,  Zhenwen Dai

IPC: H04L65/1069 ,  H04L65/613

CPC classification number: H04L65/1069 ,  H04L65/613

Abstract: Simulator augmented content selection is provided by initializing a content selection object according to session initialization parameter values associated with a simulated media content playback session. The content selection object corresponds to a candidate content selection machine learning model trained to predict selectable content media items for at least one simulated user. A simulated session including a sequence of predicted simulated user next actions and one or more predicted sets of selectable content items are generated by applying a simulated user model to content items identified by the initialized content selection object, where the simulated user model is trained to predict a next action of the simulated user in response to a simulated playback input received from the simulated user and each set of the selectable content items are correlated to each next action in the sequence of predicted simulated user next actions.

公开(公告)号：US20240236155A9

公开(公告)日：2024-07-11

申请号：US18049360

申请日：2022-10-25

Applicant: Spotify AB

Inventor： Joseph Cauteruccio ,  Mehdi Ben Ayed ,  Zhenwen Dai

IPC: H04L65/1069 ,  H04L65/613

CPC classification number: H04L65/1069 ,  H04L65/613

Abstract: Simulator augmented content selection is provided by initializing a content selection object according to session initialization parameter values associated with a simulated media content playback session. The content selection object corresponds to a candidate content selection machine learning model trained to predict selectable content media items for at least one simulated user. A simulated session including a sequence of predicted simulated user next actions and one or more predicted sets of selectable content items are generated by applying a simulated user model to content items identified by the initialized content selection object, where the simulated user model is trained to predict a next action of the simulated user in response to a simulated playback input received from the simulated user and each set of the selectable content items are correlated to each next action in the sequence of predicted simulated user next actions.

公开(公告)号：US20230419187A1

公开(公告)日：2023-12-28

申请号：US17851426

申请日：2022-06-28

Applicant: Spotify AB

Inventor： Zhenwen Dai ,  Joseph Cauteruccio ,  Federico Tomasi ,  Mehdi Ben Ayed

IPC: G06N20/10

CPC classification number: G06N20/10

Abstract: Methods, systems and computer program products are provided for content generation. A distribution of policies is defined based on an action space. Distribution parameters are received from a reinforcement learning (RL) algorithm. In turn, a policy is randomly sampled from the distribution of policies. A candidate content item is generated using the sampled policy. A quality of the candidate content item is measured based on a predefined quality criteria and a parameter model is adjusted as specified by the reinforcement learning algorithm to obtain a plurality of updated distribution parameters. Environment settings are passed to a trained parameter model to obtain a plurality of policy distribution parameters. A predetermined number of policies from the distribution of policies are then sampled and the plurality of environment settings are passed to the predetermined number of sampled policies to obtain at least one content item.