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Found 4 papers, 1 papers with code
Attention-based sequential recommendation system using multimodal data
In this study, we propose an attention-based sequential recommendation method that employs multimodal data of items such as images, texts, and categories.
NFCL: Simply interpretable neural networks for a short-term multivariate forecasting
Our proposed model, the Neural ForeCasting Layer (NFCL), employs a straightforward amalgamation of neural networks.
Neural Additive Models for Nowcasting
By employing generalized additive models, the proposed NAM-NC successfully explains each input value's importance for multiple variables and time steps.
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding.
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