Interactive Recommender Systems (IRS) have been increasingly used in various domains, including personalized article recommendation, social media, and online advertising. However, IRS faces significant challenges in providing accurate recommendations under limited observations, especially in the context of interactive collaborative filtering. These problems are exacerbated by the cold start problem and data sparsity problem. Existing Multi-Armed Bandit methods, despite their carefully designed exploration strategies, often struggle to provide satisfactory results in the early stages due to the lack of interaction data. Furthermore, these methods are computationally intractable when applied to non-linear models, limiting their applicability. To address these challenges, we propose a novel method, the Interactive Graph Convolutional Filtering model. Our proposed method extends interactive collaborative filtering into the graph model to enhance the performance of collaborative filtering between users and items. We incorporate variational inference techniques to overcome the computational hurdles posed by non-linear models. Furthermore, we employ Bayesian meta-learning methods to effectively address the cold-start problem and derive theoretical regret bounds for our proposed method, ensuring a robust performance guarantee. Extensive experimental results on three real-world datasets validate our method and demonstrate its superiority over existing baselines.

翻译：交互式推荐系统（IRS）已广泛应用于个性化文章推荐、社交媒体和在线广告等多个领域。然而，在有限观测条件下提供精准推荐仍面临重大挑战，尤其是在交互式协同过滤场景中。冷启动问题和数据稀疏性问题进一步加剧了这些难题。现有基于多臂老虎机的方法尽管设计了精巧的探索策略，但由于缺乏交互数据，往往难以在早期阶段取得满意效果。此外，这些方法在应用于非线性模型时存在计算复杂度过高的问题，限制了其适用性。为应对上述挑战，我们提出了一种创新方法——交互式图卷积过滤模型。该方法将交互式协同过滤扩展至图模型框架，以增强用户与物品间的协同过滤性能。我们引入变分推断技术，攻克非线性模型带来的计算瓶颈。同时采用贝叶斯元学习方法有效解决冷启动问题，并为所提方法推导出理论遗憾界，确保其具备稳健的性能保障。在三个真实数据集上的大量实验验证了该方法的效果，并证明其相较于现有基线方法的优越性。