60 citations · 92 across the 5 of their papers we have counts for
5 papers
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Chenglin Li, Yuanzhen Xie, Chenyun Yu +5
Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing tech…
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Guanghu Yuan, Fajie Yuan, Yudong Li +9
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets of…
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
Fan Zhou, Shikai Luo, Xiaohu Qie +2
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such co…
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Chang Liu, Jiahui Sun, Haiming Jin +7
Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the s…
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement
Lan-Zhe Guo, Feng Kuang, Zhang-Xun Liu +3
Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In…