In contrast, we argue that satisfying the users’ musical entertainment needs requires taking into account intrinsic, extrinsic, and contextual aspects of the listeners , as well as more decent interaction information. This is partly because of the fact that users’ tastes and musical needs are highly dependent on a multitude of factors, which are not considered in sufficient depth in current MRS approaches, which are typically centered on the core concept of user–item interactions, or sometimes content-based item descriptors. However, such systems are still far from being perfect and frequently produce unsatisfactory recommendations. By filtering this abundance of music items, thereby limiting choice overload , MRSs are often very successful to suggest songs that fit their users’ preferences. Thanks to music streaming services like Spotify, Pandora, or Apple Music, music aficionados are nowadays given access to tens of millions music pieces. Research in music recommender systems (MRSs) has recently experienced a substantial gain in interest both in academia and in industry . The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. We review the state of the art toward solving these challenges and discuss its limitations. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. The purpose of this trends and survey article is twofold. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user–item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. While today’s MRSs considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip.
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