7 citations · 7 across the 5 of their papers we have counts for
6 papers
RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems
Weidi Pan, He Ma, Shuhao Ye +6
The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recommender models. This paper prese…
Soft Curriculum Learning for Optimizing Fresh and Generalized Recommendations
Arnab Bhadury, Siyan Zheng, Anlan Yu +6
Large-scale recommender systems, particularly short-form video platforms, are often bottlenecked by massive popularity feedback loops. In such environments, as models recommend pop…
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
Kun Jin, James Harrison, Jiawei Li +8
Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the c…
Item-centric Exploration for Cold Start Problem
Dong Wang, Junyi Jiao, Arnab Bhadury +3
Recommender systems face a critical challenge in the item cold-start problem, which limits content diversity and exacerbates popularity bias by struggling to recommend new items. W…
Item Level Exploration Traffic Allocation in Large-scale Recommendation Systems
Dong Wang, Junyi Jiao, Arnab Bhadury +2
This paper contributes to addressing the item cold start problem in large-scale recommender systems, focusing on how to efficiently gain initial visibility for newly ingested conte…
Scaling up Dynamic Topic Models
Arnab Bhadury, Jianfei Chen, Jun Zhu +1
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data. To do posterior inference of DTMs, existing methods a…