公开(公告)号：US11782988B2

公开(公告)日：2023-10-10

申请号：US17027239

申请日：2020-09-21

Applicant: Spotify AB

Inventor： Federico Tomasi ,  Rishabh Mehrotra ,  Brian Christian Peter Brost ,  Aasish Kumar Pappu ,  Hugo Flávio Ventura Galvão ,  Mounia Lalmas-Roelleke

IPC: G06F16/9035 ,  G06F16/9038 ,  G06N5/025 ,  G06N3/04 ,  G06F18/22 ,  G06F18/214 ,  G06F18/21

CPC classification number: G06F16/9035 ,  G06F16/9038 ,  G06F18/214 ,  G06F18/2185 ,  G06F18/22 ,  G06N3/04 ,  G06N5/025

Abstract: Methods, systems and computer program products are provided for query understanding. A non-focused query quantifier generates non-focused query features that quantify a non-focused query and a non-focused query predictor generates a prediction associated with the non-focused query based on the non-focused query features.

公开(公告)号：US20220092118A1

公开(公告)日：2022-03-24

申请号：US17027239

申请日：2020-09-21

Applicant: Spotify AB

Inventor： Federico Tomasio ,  Rishabh Mehrotra ,  Brian Christian Peter Brost ,  Aasish Kumar Pappu ,  Hugo Flávio Ventura Galvão ,  Mounia Lalmas-Roelleke

IPC: G06F16/9035 ,  G06F16/9038 ,  G06K9/62 ,  G06N3/04 ,  G06N5/02

Abstract: Methods, systems and computer program products are provided for query understanding. A non-focused query quantifier generates non-focused query features that quantify a non-focused query and a non-focused query predictor generates a prediction associated with the non-focused query based on the non-focused query features.

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.