Larger training datasets continue to improve recommender system performance without observable saturation on typical user-item data.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
citing papers explorer
-
The Unreasonable Effectiveness of Data for Recommender Systems
Larger training datasets continue to improve recommender system performance without observable saturation on typical user-item data.
-
Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.