公开(公告)号：US12259950B2

公开(公告)日：2025-03-25

申请号：US17063606

申请日：2020-10-05

Applicant: Spotify AB

Inventor： Zhenwen Dai ,  Praveen Chandar Ravichandran ,  Ghazal Fazelnia ,  Benjamin Carterette ,  Mounia Lalmas-Roelleke

IPC: G06N20/00 ,  G06F18/20 ,  G06F18/21 ,  G06F18/2113 ,  G06N7/01

Abstract: Disclosed examples include an automated online experimentation mechanism that can perform model selection from a large pool of models with a relatively small number of online experiments. The probability distribution of the metric of interest that contains the model uncertainty is derived from a Bayesian surrogate model trained using historical logs. Disclosed techniques can be applied to identify a superior model by sequentially selecting and deploying a list of models from the candidate set that balance exploration-exploitation.

公开(公告)号：US11727221B2

公开(公告)日：2023-08-15

申请号：US16932323

申请日：2020-07-17

Applicant: Spotify AB

Inventor： Praveen Chandar Ravichandran ,  Mounia Lalmas-Roelleke ,  Federico Tomasi ,  Zhenwen Dai ,  Gal Levy-Fix

IPC: G06F40/44 ,  G06F40/295 ,  G06F16/35 ,  G06F40/20 ,  G06F40/30 ,  G06F17/15

CPC classification number: G06F40/44 ,  G06F16/355 ,  G06F17/15 ,  G06F40/20 ,  G06F40/295 ,  G06F40/30

Abstract: A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.

公开(公告)号：US20220019750A1

公开(公告)日：2022-01-20

申请号：US16932323

申请日：2020-07-17

Applicant: Spotify AB

Inventor： Praveen Chandar Ravichandran ,  Mounia Lalmas-Roelleke ,  Federico Tomasi ,  Zhenwen Dai ,  Gal Levy-Fix

IPC: G06F40/44 ,  G06F16/35 ,  G06F40/295 ,  G06F17/15

Abstract: A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.