activity
20172025
most citedDual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems

271 citations · 367 across the 22 of their papers we have counts for

collaborators

22 papers

cs.LG2025

Symmetry-Preserving Architecture for Multi-NUMA Environments (SPANE): A Deep Reinforcement Learning Approach for Dynamic VM Scheduling

Tin Ping Chan, Yunlong Cheng, Yizhan Zhu +2

As cloud computing continues to evolve, the adoption of multi-NUMA (Non-Uniform Memory Access) architecture by cloud service providers has introduced new challenges in virtual mach…

cs.IR2025

PathGPT: Reframing Path Recommendation as a Natural Language Generation Task with Retrieval-Augmented Language Models

Steeve Cuthbert Marcelyn, Yucen Gao, Yuzhe Zhang +1

Path recommendation (PR) aims to generate travel paths that are customized to a user's specific preferences and constraints. Conventional approaches often employ explicit optimizat…

cs.LG2024

Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making

Yang Luo, Shiyu Wang, Zhemeng Yu +4

The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient…

cs.LG2024★ 2 cited

Mixture of Link Predictors on Graphs

Li Ma, Haoyu Han, Juanhui Li +4

Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise m…

cs.IR2023★ 6 cited

Temporal Interest Network for User Response Prediction

Haolin Zhou, Junwei Pan, Xinyi Zhou +4

User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are…

cs.DC2023★ 6 cited

Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud

Tianyao Shi, Yingxuan Yang, Yunlong Cheng +3

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers wa…