Tushar Chandra

Wide & Deep Learning for Recommender Systems

2016 · arXiv (Cornell University)

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, …

SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets

2019

Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with …