1 citations · 1 across the 4 of their papers we have counts for
4 papers
LiMAML: Personalization of Deep Recommender Models via Meta Learning
Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava +10
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases cont…
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
Qiang Charles Xiao, Ajith Muralidharan, Birjodh Tiwana +4
In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequ…
Multi-objective Optimization of Notifications Using Offline Reinforcement Learning
Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang +2
Mobile notification systems play a major role in a variety of applications to communicate, send alerts and reminders to the users to inform them about news, events or messages. In…
Analysis of Fixed-Time Control
Ajith Muralidharan, Ramtin Pedarsani, Pravin Varaiya
A network of signalized intersections is modeled as a queuing network. The intersections are regulated by fixed-time (FT) controls, all with the same cycle length or period, . V…