Spotify Ab

Lucas Maystre

Rishabh Mehrotra

Mounia Lalmas-Roelleke

Federico Tomasi

Brian Christian Peter Brost

Graham Van Goffrier

Christian Hansen

Casper Hansen

Ciarán Gilligan-Lee

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.

System and method for estimating a long-term effect in the presence of unobserved confounding

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.

Systems and methods for providing media recommendations using contextual and sequential user embeddings

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London, United Kingdom

Lucas Maystre