425 citations · 639 across the 15 of their papers we have counts for
15 papers
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
Xiong-Hui Chen, Bowei He, Yang Yu +5
Long-term user engagement (LTE) optimization in sequential recommender systems (SRS) is shown to be suited by reinforcement learning (RL) which finds a policy to maximize long-term…
A Unified Representation Framework for Rideshare Marketplace Equilibrium and Efficiency
Alex Chin, Zhiwei Qin
Ridesharing platforms are a type of two-sided marketplace where ``supply-demand balance'' is critical for market efficiency and yet is complex to define and analyze. We present a u…
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
Yanqiu Wu, Qingyang Li, Zhiwei Qin
A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real…
Reinforcement Learning in the Wild: Scalable RL Dispatching Algorithm Deployed in Ridehailing Marketplace
Soheil Sadeghi Eshkevari, Xiaocheng Tang, Zhiwei Qin +4
In this study, a real-time dispatching algorithm based on reinforcement learning is proposed and for the first time, is deployed in large scale. Current dispatching methods in ride…
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
Xiaocheng Tang, Fan Zhang, Zhiwei Qin +6
Large ride-hailing platforms, such as DiDi, Uber and Lyft, connect tens of thousands of vehicles in a city to millions of ride demands throughout the day, providing great promises…
Reinforcement Learning for Ridesharing: An Extended Survey
Zhiwei Qin, Hongtu Zhu, Jieping Ye
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.…
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
Yan Jiao, Xiaocheng Tang, Zhiwei Qin +4
We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-dem…
Bayesian Meta-reinforcement Learning for Traffic Signal Control
Yayi Zou, Zhiwei Qin
In recent years, there has been increasing amount of interest around meta reinforcement learning methods for traffic signal control, which have achieved better performance compared…
Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation
Mengyue Yang, Qingyang Li, Zhiwei Qin +1
Contextual multi-armed bandit (MAB) achieves cutting-edge performance on a variety of problems. When it comes to real-world scenarios such as recommendation system and online adver…
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
John Holler, Risto Vuorio, Zhiwei Qin +6
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform…
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
Lingyu Zhang, Xu Geng, Zhiwei Qin +7
Graph convolution network based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship repr…
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
Ishan Jindal, Zhiwei Qin, Xuewen Chen +2
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are req…
Robust Low-rank Tensor Recovery: Models and Algorithms
Donald Goldfarb, Zhiwei Qin
Robust tensor recovery plays an instrumental role in robustifying tensor decompositions for multilinear data analysis against outliers, gross corruptions and missing values and has…
An Alternating Direction Method for Total Variation Denoising
Zhiwei Qin, Donald Goldfarb, Shiqian Ma
We consider the image denoising problem using total variation (TV) regularization. This problem can be computationally challenging to solve due to the non-differentiability and non…
Structured Sparsity via Alternating Direction Methods
Zhiwei Qin, Donald Goldfarb
We consider a class of sparse learning problems in high dimensional feature space regularized by a structured sparsity-inducing norm which incorporates prior knowledge of the group…