Current challenges and visions in music recommender systems research

Abstract

Music recommender systems (MRS) 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 ngertip. While today's MRS considerably help users to nd interesting music in these huge catalogs, MRS research is still facing substantial challenges. 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. e purpose of this trends and survey article is twofold. We rst identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the eld. e 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 eld.

Emotion-Matched Music Increases Cardiovascular Engagement Without Elevating Perceived Exertion During Self-Paced Cycling

“…Most existing emotion-aware music systems are evaluated using prediction accuracy or user satisfaction metrics rather than objective physiological or behavioural outcomes (Schedl et al, 2018;Garcia-Gathright et al, 2018). The present findings extend this evaluation framework by demonstrating that Phiona's emotion-based music matching influences how effort is experienced during physical activity, as reflected in physiological engagement and perceived exertion under self-paced conditions.…”

Emotion-Matched Music Increases Cardiovascular Engagement Without Elevating Perceived Exertion During Self-Paced Cycling

“…Most existing emotion-aware music systems are evaluated using prediction accuracy or user satisfaction metrics rather than objective physiological or behavioural outcomes (Schedl et al, 2018;Garcia-Gathright et al, 2018). The present findings extend this evaluation framework by demonstrating that Phiona's emotion-based music matching influences how effort is experienced during physical activity, as reflected in physiological engagement and perceived exertion under self-paced conditions.…”

A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services

“…While Table 1 supports these assumptions, more investigations will be required in future work for confirmation. Our test also encourages the development of an improved Mix-Transformer addressing popularity biases [21] on regular users, e.g., by controlling popularity levels in the training dataset and the optimized loss.…”

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

“…Performance metrics : To quantify the accuracy of the recommendations, we use the following metrics (similar to Liang et al, 2018 ; Schedl et al, 2018 ; Aiolli, 2013 ), which we report averaged over all users (in the test set). Thus, for each user in the test set, we generate recommendations using the data in the training set and compare the recommended tracks with the actually listened tracks of the user present in the test set in order to calculate the performance metrics.…”

Emotion-Matched Music Increases Cardiovascular Engagement Without Elevating Perceived Exertion During Self-Paced Cycling

“…Most existing emotion-aware music systems are evaluated using prediction accuracy or user satisfaction metrics rather than objective physiological or behavioural outcomes (Schedl et al, 2018;Garcia-Gathright et al, 2018). The present findings extend this evaluation framework by demonstrating that Phiona's emotion-based music matching influences how effort is experienced during physical activity, as reflected in physiological engagement and perceived exertion under self-paced conditions.…”

A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services

“…While Table 1 supports these assumptions, more investigations will be required in future work for confirmation. Our test also encourages the development of an improved Mix-Transformer addressing popularity biases [21] on regular users, e.g., by controlling popularity levels in the training dataset and the optimized loss.…”

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

“…Performance metrics : To quantify the accuracy of the recommendations, we use the following metrics (similar to Liang et al, 2018 ; Schedl et al, 2018 ; Aiolli, 2013 ), which we report averaged over all users (in the test set). Thus, for each user in the test set, we generate recommendations using the data in the training set and compare the recommended tracks with the actually listened tracks of the user present in the test set in order to calculate the performance metrics.…”

Emotion-Matched Music Increases Cardiovascular Engagement Without Elevating Perceived Exertion During Self-Paced Cycling

“…Most existing emotion-aware music systems are evaluated using prediction accuracy or user satisfaction metrics rather than objective physiological or behavioural outcomes (Schedl et al, 2018;Garcia-Gathright et al, 2018). The present findings extend this evaluation framework by demonstrating that Phiona's emotion-based music matching influences how effort is experienced during physical activity, as reflected in physiological engagement and perceived exertion under self-paced conditions.…”

A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services

“…While Table 1 supports these assumptions, more investigations will be required in future work for confirmation. Our test also encourages the development of an improved Mix-Transformer addressing popularity biases [21] on regular users, e.g., by controlling popularity levels in the training dataset and the optimized loss.…”

Listener Modeling and Context-Aware Music Recommendation Based on Country Archetypes

“…Performance metrics : To quantify the accuracy of the recommendations, we use the following metrics (similar to Liang et al, 2018 ; Schedl et al, 2018 ; Aiolli, 2013 ), which we report averaged over all users (in the test set). Thus, for each user in the test set, we generate recommendations using the data in the training set and compare the recommended tracks with the actually listened tracks of the user present in the test set in order to calculate the performance metrics.…”