239 citations · 890 across the 14 of their papers we have counts for
14 papers
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Xin Zhou, Aixin Sun, Yong Liu +2
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt nega…
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 Critical Study on Data Leakage in Recommender System Offline Evaluation
Yitong Ji, Aixin Sun, Jie Zhang +1
Recommender models are hard to evaluate, particularly under offline setting. In this paper, we provide a comprehensive and critical analysis of the data leakage issue in recommende…
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 Variable Ordering Heuristics for Solving Constraint Satisfaction Problems
Wen Song, Zhiguang Cao, Jie Zhang +1
Backtracking search algorithms are often used to solve the Constraint Satisfaction Problem (CSP). The efficiency of backtracking search depends greatly on the variable ordering heu…
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…
Hierarchical Attentive Knowledge Graph Embedding for Personalized Recommendation
Xiao Sha, Zhu Sun, Jie Zhang
Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich and complementary information to u…
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…
Exploring Communities in Large Profiled Graphs
Yankai Chen, Yixiang Fang, Reynold Cheng +3
Given a graph and a vertex , the community search (CS) problem aims to efficiently find a subgraph of whose vertices are closely related to . Communities are pre…
Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
Zehong Hu, Yitao Liang, Yang Liu +1
Incentive mechanisms for crowdsourcing are designed to incentivize financially self-interested workers to generate and report high-quality labels. Existing mechanisms are often dev…
Interacting Attention-gated Recurrent Networks for Recommendation
Wenjie Pei, Jie Yang, Zhu Sun +3
Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history…
Scaling POMDPs For Selecting Sellers in E-markets-Extended Version
Athirai A. Irissappane, Frans A. Oliehoek, Jie Zhang
In multiagent e-marketplaces, buying agents need to select good sellers by querying other buyers (called advisors). Partially Observable Markov Decision Processes (POMDPs) have sho…