Explainable recommender system that maximizes exploration
Homanga Bharadhwaj · 2019
In this work, we illustrate the design of an explainable recommendation framework that seeks to maximize exploration while maintaining recommendation relevance. We use Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNNs) to model the temporal evolution of users' preference for items. We define an objective metric for exploration and frame the overall optimization loss function to incorporate exploration as a regularizer. We report evaluations on state of the art Netflix and IMDb datasets, and highlight future research agenda for developing an interactive framework based on the proposed algorithm.