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.

翻译：在这项工作中,我们研究以背景多武装土匪问题(MAB)为样板的建议系统,我们提出一个基于图表的建议系统,以学习和利用用户空间的几何学,在用户领域创建有意义的群集,从而减少建议问题的维度,同时保持MAB的准确性。然后我们研究图散和群集大小对MAB性能的影响,并在合成和真实数据集中提供详尽的模拟结果。模拟结果显示最新MAB算法的改进。