A comprehensive survey on multimodal recommender systems: Taxonomy, evaluation, and future directions
Estimating package arrival time via heterogeneous hypergraph neural network
Abstract Estimated Time of Arrival (ETA) for packages plays an essential role in intelligent
logistics. As a classic ETA method, Origin–Destination-based (OD-based) ETA predicts the …
logistics. As a classic ETA method, Origin–Destination-based (OD-based) ETA predicts the …
[HTML][HTML] Bayesian Modeling of Travel Times on the Example of Food Delivery: Part 2—Model Creation and Handling Uncertainty
J Pomykacz, J Gibas, J Baranowski - Electronics, 2024 - mdpi.com
The e-commerce sector is in a constant state of growth and evolution, particularly within its
subdomain of online food delivery. As such, ensuring customer satisfaction is critical for …
subdomain of online food delivery. As such, ensuring customer satisfaction is critical for …
Delivery time prediction using large-scale graph structure learning based on quantile regression
Predicting Estimated Time of Arrival (ETA) for packages is a critical problem in e-commerce.
The prediction is often made based on spatial (sending and receiving addresses), temporal …
The prediction is often made based on spatial (sending and receiving addresses), temporal …
DEER: Distribution Divergence-based Graph Contrast for Partial Label Learning on Graphs
Graph neural networks (GNNs) have emerged as powerful tools for graph classification
tasks. However, contemporary graph classification methods are predominantly studied in …
tasks. However, contemporary graph classification methods are predominantly studied in …
Dual graph multitask framework for imbalanced delivery time estimation
Abstract Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply
chain that predicts delivery time based on merchant information, sending address, receiving …
chain that predicts delivery time based on merchant information, sending address, receiving …