54 citations · 98 across the 5 of their papers we have counts for
9 papers
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems
Liang Xin, Wen Song, Zhiguang Cao +1
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM)…
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Cong Zhang, Wen Song, Zhiguang Cao +3
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myria…
A Re-visit of the Popularity Baseline in Recommender Systems
Yitong Ji, Aixin Sun, Jie Zhang +1
Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated…
COBRA: Context-aware Bernoulli Neural Networks for Reputation Assessment
Leonit Zeynalvand, Tie Luo, Jie Zhang
Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT). On…
Learning Improvement Heuristics for Solving Routing Problems
Yaoxin Wu, Wen Song, Zhiguang Cao +2
Recent studies in using deep learning to solve routing problems focus on construction heuristics, the solutions of which are still far from optimality. Improvement heuristics have…
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
Zhu Sun, Qing Guo, Jie Yang +4
Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data spa…