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Found 7 papers, 1 papers with code
Robust Representation Learning for Unified Online Top-K Recommendation
The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations.
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker.
DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction
Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.
LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry
In this paper, we introduce \texttt{LaDe}, the first publicly available last-mile delivery dataset with millions of packages from the industry.
G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System
How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system.
AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction
Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously.
An end-to-end predict-then-optimize clustering method for intelligent assignment problems in express systems
To solve these problems, we put forward an intelligent end-to-end predict-then-optimize clustering method to simultaneously predict the future pick-up requests of AOIs and assign AOIs to couriers by clustering.
