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Found 4 papers, 1 papers with code
Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID
The exponential growth of online content has posed significant challenges to ID-based models in industrial recommendation systems, ranging from extremely high cardinality and dynamically growing ID space, to highly skewed engagement distributions, to prediction instability as a result of natural id life cycles (e. g, the birth of new IDs and retirement of old IDs).
Hierarchical Structured Neural Network: Efficient Retrieval Scaling for Large Scale Recommendation
In this paper, we introduce the Hierarchical Structured Neural Network (HSNN), an efficient deep neural network model to learn intricate user and item interactions beyond the commonly used dot product in retrieval tasks, achieving sublinear computational costs relative to corpus size.
A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
It constructs a block-diagonal preconditioner where each block consists of a coarse Kronecker product approximation to full-matrix AdaGrad for each parameter of the neural network.
NxtPost: User to Post Recommendations in Facebook Groups
In this paper, we present NxtPost, a deployed user-to-post content-based sequential recommender system for Facebook Groups.
