34 citations · 57 across the 4 of their papers we have counts for
6 papers
FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization
Arundeep Chinta, Lucas Vinh Tran, Jay Katukuri
Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations and the need to prevent "ri…
Inverse Theory of Mind Modeling for Content Recommendation: From Web Browsing to Dynamic Intelligent Interfaces
Mengyu Chen, Feiyu Lu, Chun-Fu Chen +2
Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rather than stable preference e…
ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition
Arundeep Chinta, Lucas Vinh Tran, Jay Katukuri
Time Series Foundation Models (TSFMs) have emerged as a promising approach for zero-shot financial forecasting, demonstrating strong transferability and data efficiency gains. Howe…
Quaternion Collaborative Filtering for Recommendation
Shuai Zhang, Lina Yao, Lucas Vinh Tran +2
This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Qu…
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
Lucas Vinh Tran, Yi Tay, Shuai Zhang +2
This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic spa…
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay +3
This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challen…