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

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

Question Relevance in Visual Question Answering

no code implementations • 23 Jul 2018 • Prakruthi Prabhakar, Nitish Kulkarni, Linghao Zhang

Current VQA systems do not evaluate if the posed question is relevant to the input image and hence provide nonsensical answers when posed with irrelevant questions to an image.

Question Answering Visual Question Answering