Personalization and User Modeling

We design ranking methods that are efficient, explainable, and fair in order to understand user behavior in the context of personalized recommender systems.

• A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation.
  Yue Ning, Yue Shi, Liangjie Hong, Huzefa Rangwala, Naren Ramakrishnan.
  In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17).
  [ pdf | slides | bibtex ]