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In this paper we provide an overview of the approach we used as team CreamyFireflies for the ACM RecSys Challenge 2018. The competition, organized bySpotify, focuses on the problem of playlist continuation, that is suggestingwhich tracks the user may add to an existing playlist. The challenge addressesthis issue in many use cases, from playlist cold start to playlists alreadycomposed by up to a hundred tracks. Our team proposes a solution based on a fewwell known models both content based and collaborative, whose predictions areaggregated via an ensembling step. Moreover by analyzing the underlyingstructure of the data, we propose a series of boosts to be applied on top ofthe final predictions and improve the recommendation quality. The proposedapproach leverages well-known algorithms and is able to offer a highrecommendation quality while requiring a limited amount of computationalresources.

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