Artist-driven layering and user's behaviour impact on recommendations in
a playlist continuation scenario
- URL: http://arxiv.org/abs/2010.06233v1
- Date: Tue, 13 Oct 2020 08:47:08 GMT
- Title: Artist-driven layering and user's behaviour impact on recommendations in
a playlist continuation scenario
- Authors: Sebastiano Antenucci, Simone Boglio, Emanuele Chioso, Ervin Dervishaj,
Shuwen Kang, Tommaso Scarlatti, Maurizio Ferrari Dacrema
- Abstract summary: We provide an overview of the approach we used as team Creamy Fireflies for the ACM RecSys Challenge 2018.
The competition, organized by Spotify, focuses on the problem of playlist continuation.
Our team proposes a solution based on a few well known models both content based and collaborative.
- Score: 5.726366023100079
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we provide an overview of the approach we used as team Creamy
Fireflies for the ACM RecSys Challenge 2018. The competition, organized by
Spotify, focuses on the problem of playlist continuation, that is suggesting
which tracks the user may add to an existing playlist. The challenge addresses
this issue in many use cases, from playlist cold start to playlists already
composed by up to a hundred tracks. Our team proposes a solution based on a few
well known models both content based and collaborative, whose predictions are
aggregated via an ensembling step. Moreover by analyzing the underlying
structure of the data, we propose a series of boosts to be applied on top of
the final predictions and improve the recommendation quality. The proposed
approach leverages well-known algorithms and is able to offer a high
recommendation quality while requiring a limited amount of computational
resources.
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