User models for multi-context-aware music recommendation
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User models for multi-context-aware music recommendation Martin Pichl1 · Eva Zangerle1 Received: 1 March 2019 / Revised: 24 June 2020 / Accepted: 16 September 2020 / © The Author(s) 2020
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-contextaware 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 multicontext-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems. Keywords Recommender systems · Context-aware recommender systems · Personalization · User modeling
1 Introduction Over the last decade, people have increasingly started to use music streaming platforms providing millions of tracks [32]. Streaming platforms heavily rely on recommender systems to help users navigate through the provided collections and discover music they
Eva Zangerle
[email protected] 1
Department of Computer Science, University of Innsbruck, Innsbruck, Austria
Multimedia Tools and Applications
like. However, the extent to which a user enjoys and likes a recommended song heavily depends on the user’s current context. Previous research has shown that information about the context of a user (e.g., time, location, occasion, or emotional state) is vital for providing suitable personalized music recommendations [27, 31] as people listen to different music during different activities [23]. Also, Cunningham et al. [13] have shown that users create playlists that are specifically intended for certain contexts or activities. Extracting contextual information for a music recommendation scenario, however, 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, ultimately allowing to find playlists that users created for similar purposes and situations [42, 44]. We proposed to leverage these situational clusters as an additiona
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