论文标题
土匪在影响下（扩展版）
Bandits Under The Influence (Extended Version)
论文作者
论文摘要
随着后者的发展，推荐系统应适应用户兴趣。用户利益发展的普遍原因是他们社交圈的影响。通常，当兴趣不知道时，探索推荐空间的同时利用观察到的偏好的在线算法是可取的。我们介绍了植根于线性多臂强盗文献的在线推荐算法。我们的强盗算法精确地量身定制，以推荐用户利益在社会影响下发展的方案。特别是，我们表明我们对经典的Linrel和Thompson采样算法的适应性保持与非社会案例相同的渐近遗憾界限。我们使用合成数据集对实验验证我们的方法。
Recommender systems should adapt to user interests as the latter evolve. A prevalent cause for the evolution of user interests is the influence of their social circle. In general, when the interests are not known, online algorithms that explore the recommendation space while also exploiting observed preferences are preferable. We present online recommendation algorithms rooted in the linear multi-armed bandit literature. Our bandit algorithms are tailored precisely to recommendation scenarios where user interests evolve under social influence. In particular, we show that our adaptations of the classic LinREL and Thompson Sampling algorithms maintain the same asymptotic regret bounds as in the non-social case. We validate our approach experimentally using both synthetic and real datasets.
