公开(公告)号：US12137148B1

公开(公告)日：2024-11-05

申请号：US18351893

申请日：2023-07-13

Applicant: Spotify AB

Inventor： Ciarán Gilligan-Lee ,  Lucas Maystre ,  Graham Van Goffrier

IPC: H04L67/306 ,  H04L67/00

Abstract: Systems and methods for estimating a long-term effect in the presence of unobserved confounding are provided. For example, a long-term effect of a change in user interface of a media playback application may be estimated. An experimental dataset and an observational dataset may be compiled using data from a plurality of computing devices. The observational dataset may include unobserved confounding. Using the experimental dataset, a short-term effect may be determined. Using the short-term effect and samples from the observational dataset, an instrumental variable may be computed that may be used in instrumental variable regression to estimate the long-term effect.

公开(公告)号：US20230075530A1

公开(公告)日：2023-03-09

申请号：US17465417

申请日：2021-09-02

Applicant: Spotify AB

Inventor： Judith Bütepage ,  Lucas Maystre

IPC: G06N3/08 ,  G06N3/04

Abstract: A method comprises the following steps: providing a Gaussian process variational autoencoder (GP-VAE) including a Gaussian process (GP) encoder and a neural network decoder; selecting a plurality of inducing points in a data space; generating a mapping of the plurality of inducing points in a latent space; and training the GP-VAE using a training dataset.

公开(公告)号：US11782968B2

公开(公告)日：2023-10-10

申请号：US16789214

申请日：2020-02-12

Applicant: Spotify AB

Inventor： Casper Hansen ,  Christian Hansen ,  Lucas Maystre ,  Rishabh Mehrotra ,  Brian Christian Peter Brost ,  Federico Tomasi ,  Mounia Lalmas-Roelleke

IPC: G06F16/435 ,  G06F16/438 ,  G06F16/41 ,  H04L65/60 ,  G06N3/08 ,  G06F16/2457

CPC classification number: G06F16/435 ,  G06F16/24575 ,  G06F16/41 ,  G06F16/438 ,  G06N3/08 ,  H04L65/60

Abstract: An electronic device stores a plurality of vector representations for respective media content items in a vector space, where each vector represents a media content item. The electronic device receives a first set of input parameters representing a previous session of a user of the media-providing service where the previous session included two or more of the respective media content items. The electronic device then receives a second set of input parameters representing a current context of the user and provides the first set of input parameters and the second set of input parameters to a neural network to generate a prediction vector for a current session. The prediction vector is embedded in the vector space. The electronic device identifies, based on the prediction vector for the current session, a plurality of media content items of the respective media content items in the vector space and provides the plurality of media content items to the user of the media-providing service during the current session.

公开(公告)号：US20220012565A1

公开(公告)日：2022-01-13

申请号：US17320439

申请日：2021-05-14

Applicant: Spotify AB

Inventor： Christian Hansen ,  Casper Hansen ,  Brian Christian Peter Brost ,  Lucas Maystre ,  Mounia Lalmas-Roelleke ,  Rishabh Mehrotra

IPC: G06N3/04 ,  G06N3/08

Abstract: A reinforcement learning ranker can take into account previously-recommended media content items to produce a ranked list of media content items to recommend next. The ranker finds a policy that gives the probability of sampling a media content item given a state. The policy is learned such that it maximizes a reward. A reward function associated with the media content item can be defined with respect to whether the user finds the media content item relevant (likelihood that the user will like the media content item) and a diversity score of the media content item.