公开(公告)号：US20210168184A1

公开(公告)日：2021-06-03

申请号：US17174317

申请日：2021-02-11

Applicant: Netflix, Inc.

Inventor： Mohammad Hossein Taghavi ,  Prasanna Padmanabhan ,  Dong-Bang Tsai ,  Faisal Zakaria Siddiqi ,  Justin Derrick Basilico

IPC: H04L29/06 ,  H04L29/08 ,  G06N5/04 ,  G06N20/00

Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

公开(公告)号：US11522938B2

公开(公告)日：2022-12-06

申请号：US17174317

申请日：2021-02-11

Applicant: Netflix, Inc.

Inventor： Mohammad Hossein Taghavi ,  Prasanna Padmanabhan ,  Dong-Bang Tsai ,  Faisal Zakaria Siddiqi ,  Justin Derrick Basilico

IPC: H04L65/75 ,  G06N5/04 ,  G06N20/00 ,  H04L65/70 ,  H04L67/50 ,  H04L67/306 ,  H04N21/24 ,  G06F16/335 ,  G06F16/9535 ,  H04L67/303 ,  H04N21/25 ,  H04N21/258

Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

公开(公告)号：US20190394252A1

公开(公告)日：2019-12-26

申请号：US16557558

申请日：2019-08-30

Applicant: Netflix, Inc.

Inventor： Mohammad Hossein Taghavi ,  Prasanna Padmanabhan ,  Dong-Bang Tsai ,  Faisal Zakaria Siddiqi ,  Justin Derrick Basilico

IPC: H04L29/06 ,  H04L29/08 ,  G06N5/04 ,  G06N20/00

Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

公开(公告)号：US10958704B2

公开(公告)日：2021-03-23

申请号：US16557558

申请日：2019-08-30

Applicant: Netflix, Inc.

Inventor： Mohammad Hossein Taghavi ,  Prasanna Padmanabhan ,  Dong-Bang Tsai ,  Faisal Zakaria Siddiqi ,  Justin Derrick Basilico

IPC: H04L29/06 ,  G06N5/04 ,  G06F16/335 ,  H04L29/08 ,  G06N20/00 ,  H04N21/24 ,  G06F16/9535 ,  H04N21/25 ,  H04N21/258

Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

公开(公告)号：US10432689B2

公开(公告)日：2019-10-01

申请号：US15331106

申请日：2016-10-21

Applicant: NETFLIX, Inc

Inventor： Mohammad Hossein Taghavi ,  Prasanna Padmanabhan ,  Dong-Bang Tsai ,  Faisal Zakaria Siddiqi ,  Justin Derrick Basilico

IPC: H04L29/06 ,  H04L29/08 ,  G06N5/04 ,  G06N20/00 ,  H04N21/24 ,  G06F16/335 ,  G06F16/9535 ,  H04N21/25 ,  H04N21/258

Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.