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Found 5 papers, 1 papers with code
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation
In this study, we investigate the use of transformer-based self-attentive architectures to learn implicit session-level information for sequential music recommendation.
Content-driven Music Recommendation: Evolution, State of the Art, and Challenges
Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content.
Unsupervised Cross-Modal Audio Representation Learning from Unstructured Multilingual Text
We present an approach to unsupervised audio representation learning.
Session-Based Hotel Recommendations: Challenges and Future Directions
In the year 2019, the Recommender Systems Challenge deals with a real-world task from the area of e-tourism for the first time, namely the recommendation of hotels in booking sessions.
Towards multi-instrument drum transcription
In this work, convolutional and convolutional recurrent neural networks are trained to transcribe a wider range of drum instruments.
