Long-Term Embeddings anchor sequential recommendation models to fixed content-based item representations to capture stable preferences and ensure version compatibility, resulting in uplifts in user engagement and financial metrics.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
CCE- is a Triton kernel implementation of cross-entropy loss with negative sampling that reduces memory by more than 10x and accelerates training by up to 2x for large-catalog sequential recommenders.
citing papers explorer
-
Long-Term Embeddings for Balanced Personalization
Long-Term Embeddings anchor sequential recommendation models to fixed content-based item representations to capture stable preferences and ensure version compatibility, resulting in uplifts in user engagement and financial metrics.
-
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
CCE- is a Triton kernel implementation of cross-entropy loss with negative sampling that reduces memory by more than 10x and accelerates training by up to 2x for large-catalog sequential recommenders.