Graph-Based Recommendation System
This work addresses recommendation systems for users by proposing an incremental method that enhances MAB performance through graph-based clustering.
The authors tackled the problem of recommendation systems by modeling them as contextual multi-armed bandit problems, proposing a graph-based system that clusters users to reduce dimensionality while maintaining accuracy, and simulation results showed improvements over state-of-the-art MAB algorithms.
In this work, we study recommendation systems modelled as contextual multi-armed bandit (MAB) problems. We propose a graph-based recommendation system that learns and exploits the geometry of the user space to create meaningful clusters in the user domain. This reduces the dimensionality of the recommendation problem while preserving the accuracy of MAB. We then study the effect of graph sparsity and clusters size on the MAB performance and provide exhaustive simulation results both in synthetic and in real-case datasets. Simulation results show improvements with respect to state-of-the-art MAB algorithms.