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Found 4 papers, 3 papers with code
Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
Large-scale recommendation systems are characterized by their reliance on high cardinality, heterogeneous features and the need to handle tens of billions of user actions on a daily basis.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(HR@10 (full corpus) metric)
Revisiting Neural Retrieval on Accelerators
A key component of retrieval is to model (user, item) similarity, which is commonly represented as the dot product of two learned embeddings.
Model-Agnostic Graph Regularization for Few-Shot Learning
In this paper, we present a comprehensive empirical study on graph embedded few-shot learning.
Generating Representative Headlines for News Stories
In this work, we study the problem of generating representative headlines for news stories.
