Context-aware Preference Modeling with Factorization

Abstract

This work focuses on solving the context-aware implicit feedback based recommendation task with factorization and is heavily influenced by the practical considerations. I propose context-aware factorization algorithms that can efficiently work on implicit data. I generalize these algorithms and propose the General Factorization Framework (GFF) in which experimentation with novel preference models is possible. This practically useful, yet neglected feature results in models that are more appropriate for context-aware recommendations than the ones used by the state-of-the-art. I also propose a way to speed up and enhance scalability of the training process, that makes it viable to use the more accurate high factor models with reasonable training times.