3 citations · 3 across the 2 of their papers we have counts for
3 papers
SlimPer: Make Personalization Model Slim and Smart
Siqi Wang, Xianjie Chen, Shaofeng Deng +42
Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely o…
Semantic IDs for Music Recommendation
M. Jeffrey Mei, Florian Henkel, Samuel E. Sandberg +2
Training recommender systems for next-item recommendation often requires unique embeddings to be learned for each item, which may take up most of the trainable parameters for a mod…
Negative Feedback for Music Personalization
M. Jeffrey Mei, Oliver Bembom, Andreas F. Ehmann
Next-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 i…