Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario

TL;DR

This paper presents a playlist continuation approach combining content-based and collaborative models with data-driven boosts, achieving high-quality recommendations with limited computational resources.

Contribution

The authors introduce an ensemble-based playlist continuation method enhanced by data structure analysis and boosting techniques, tailored for the ACM RecSys Challenge 2018.

Findings

High recommendation quality achieved

Effective ensemble of models used

Boosting techniques improve predictions

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.