6 citations · 7 across the 6 of their papers we have counts for
6 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…
Offline Reinforcement Learning for Mobile Notifications
Yiping Yuan, Ajith Muralidharan, Preetam Nandy +2
Mobile notification systems have taken a major role in driving and maintaining user engagement for online platforms. They are interesting recommender systems to machine learning pr…
PointQ model of an arterial network: calibration and experiments
Fatma Yildiz Tascikaraoglu, Jennie Lioris, Ajith Muralidharan +2
The calibration of a PointQ arterial microsimulation model is formulated as a quadratic programming problem (QP) whose decision variables are link flows, demands at entry links, an…
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…