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.

提出了一种名为 “交互图卷积过滤模型” 的新颖方法，通过扩展交互式协同过滤到图模型中，增强用户和物品之间的协同过滤性能，同时采用变分推断技术解决非线性模型的计算难题，以及采用贝叶斯元学习方法有效解决冷启动问题，并对该方法建立理论上的遗憾界限，确保其稳健性能。在三个真实数据集上的广泛实验结果验证了该方法的优越性。