High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems, only leverage recognized user interests, falling short when it comes to efficiently uncovering unknown user preferences. While there has been some progress with neural contextual bandit algorithms towards enabling online exploration through neural networks, their onerous computational demands hinder widespread adoption in real-world recommender systems. In this work, we propose a scalable sample-efficient neural contextual bandit algorithm for recommender systems. To do this, we design an epistemic neural network architecture, Epistemic Neural Recommendation (ENR), that enables Thompson sampling at a large scale. In two distinct large-scale experiments with real-world tasks, ENR significantly boosts click-through rates and user ratings by at least 9% and 6% respectively compared to state-of-the-art neural contextual bandit algorithms. Furthermore, it achieves equivalent performance with at least 29% fewer user interactions compared to the best-performing baseline algorithm. Remarkably, while accomplishing these improvements, ENR demands orders of magnitude fewer computational resources than neural contextual bandit baseline algorithms.

翻译：高质量推荐系统应通过有效的探索性交互，向用户传递既创新又相关的内容。然而，作为现有推荐系统核心的基于监督学习的神经网络仅能利用已知的用户兴趣，在高效发现未知用户偏好方面存在不足。尽管神经上下文赌博机算法在通过神经网络实现在线探索方面取得了一定进展，但其高昂的计算需求阻碍了在真实推荐系统中的广泛应用。本文针对推荐系统提出了一种可扩展且样本高效的神经上下文赌博机算法。为此，我们设计了一种认知神经网络架构——认知神经推荐（ENR），该架构支持大规模汤普森采样。在两个不同真实场景的大规模实验中，与最先进的神经上下文赌博机算法相比，ENR将点击率和用户评分分别至少提升9%和6%。此外，与表现最佳的基线算法相比，ENR在实现同等性能时所需的用户交互次数至少减少29%。值得注意的是，在实现这些改进的同时，ENR所需的计算资源比神经上下文赌博机基线算法低数个数量级。