2 citations · 3 across the 3 of their papers we have counts for
5 papers
Learning Recommendations While Influencing Interests
Rahul Meshram, D. Manjunath, Nikhil Karamchandani
Personalized recommendation systems (RS) are extensively used in many services. Many of these are based on learning algorithms where the RS uses the recommendation history and the…
Sequential Decision Making with Limited Observation Capability: Application to Wireless Networks
Kesav Kaza, Rahul Meshram, Varun Mehta +1
This work studies a generalized class of restless multi-armed bandits with hidden states and allow cumulative feedback, as opposed to the conventional instantaneous feedback. We ca…
A Hidden Markov Restless Multi-armed Bandit Model for Playout Recommendation Systems
Rahul Meshram, Aditya Gopalan, D. Manjunath
We consider a restless multi-armed bandit (RMAB) in which there are two types of arms, say A and B. Each arm can be in one of two states, say or Playing a type A arm bring…
Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs
Rahul Meshram, Aditya Gopalan, D. Manjunath
We describe and study a model for an Automated Online Recommendation System (AORS) in which a user's preferences can be time-dependent and can also depend on the history of past re…
On the Whittle Index for Restless Multi-armed Hidden Markov Bandits
Rahul Meshram, D. Manjunath, Aditya Gopalan
We consider a restless multi-armed bandit in which each arm can be in one of two states. When an arm is sampled, the state of the arm is not available to the sampler. Instead, a bi…