LiMAML: Personalization of Deep Recommender Models via Meta Learning

no code implementations • 23 Feb 2024 • Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, Sathiya Keerthi, Ajith Muralidharan

In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives.

Meta-Learning Recommendation Systems

MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems

no code implementations • 11 Jan 2024 • Qiang Charles Xiao, Ajith Muralidharan, Birjodh Tiwana, Johnson Jia, Fedor Borisyuk, Aman Gupta, Dawn Woodard

In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness.

OpenAI Gym Recommendation Systems +1

Multi-objective Optimization of Notifications Using Offline Reinforcement Learning

no code implementations • 7 Jul 2022 • Prakruthi Prabhakar, Yiping Yuan, Guangyu Yang, Wensheng Sun, Ajith Muralidharan

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.

Q-Learning reinforcement-learning +1

Offline Reinforcement Learning for Mobile Notifications

no code implementations • 4 Feb 2022 • Yiping Yuan, Ajith Muralidharan, Preetam Nandy, Miao Cheng, Prakruthi Prabhakar

Mobile notification systems have taken a major role in driving and maintaining user engagement for online platforms.

Attribute Recommendation Systems +2