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- short-paperJune 2024
Negative Feedback for Music Personalization
UMAP '24: Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and PersonalizationJune 2024, Pages 195–200https://doi.org/10.1145/3627043.3659553Next-item recommender systems are often trained using only positive feedback with randomly-sampled negative feedback. We show the benefits of using real negative feedback both as inputs into the user sequence and also as negative targets for training a ...
- abstractSeptember 2023
Station and Track Attribute-Aware Music Personalization
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsSeptember 2023, Pages 1031–1035https://doi.org/10.1145/3604915.3610239We present a transformer for music personalization that recommends tracks given a station seed (artist) and improves the accuracy vs. a baseline matrix factorization method by 10%. Adding additional embeddings to capture track and station attributes ...