Abstract
In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.
Highlights
Over the last decade, people have increasingly started to use music streaming platforms providing millions of tracks [32]
We show that a recommender system leveraging this proposed model substantially outperforms context-agnostic baselines and, more importantly, a context-aware recommender system that relies on either context- or acoustic feature-based clusters individually
We evaluate a recommendation list containing all recommendations (i.e., n = R, the number of tracks in the test set for a given user)
Summary
People have increasingly started to use music streaming platforms providing millions of tracks [32]. Extracting contextual information for a music recommendation scenario, is a complex task To this end, in previous work we proposed an approach for clustering contextually similar playlists by extracting contextual information from the names of playlists, allowing to find playlists that users created for similar purposes and situations [42, 44]. What is still missing, is linking information about the situational context of a user with acoustic feature-based playlist archetypes that represent different types of music that users listen to. We propose to make use of Factorization Machines (FM) [46] as these allow for exploiting latent features and interactions between input variables This allows us to exploit interaction effects between contextual clusters extracted from the names of playlists and acoustic clusters based on audio characteristics. We show that a recommender system leveraging this proposed model substantially outperforms context-agnostic baselines and, more importantly, a context-aware recommender system that relies on either context- or acoustic feature-based clusters individually
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