Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommendation using training-based collaborative filtering, requiring consideration of multiple ratings compared to single-criterion counterparts, often poses practical challenges in achieving state-of-the-art performance along with scalable model training. To solve this problem, we propose CA-GF, a training-free MC recommendation method, which is built upon criteria-aware graph filtering for efficient yet accurate MC recommendations. Specifically, first, we construct an item-item similarity graph using an MC user-expansion graph. Next, we design CA-GF composed of the following key components, including 1) criterion-specific graph filtering where the optimal filter for each criterion is found using various types of polynomial low-pass filters and 2) criteria preference-infused aggregation where the smoothed signals from each criterion are aggregated. We demonstrate that CA-GF is (a) efficient: providing the computational efficiency, offering the extremely fast runtime of less than 0.2 seconds even on the largest benchmark dataset, (b) accurate: outperforming benchmark MC recommendation methods, achieving substantial accuracy gains up to 24% compared to the best competitor, and (c) interpretable: providing interpretations for the contribution of each criterion to the model prediction based on visualizations.

翻译：多准则（MC）推荐系统利用多准则评分信息进行推荐，在各类电子商务领域中日益普及。然而，基于训练协同过滤的MC推荐方法相较于单准则方法需考虑多个评分维度，在实现最优性能与可扩展模型训练方面常面临实际挑战。为解决此问题，我们提出CA-GF——一种无需训练的MC推荐方法，该方法基于标准感知图滤波技术，实现高效而准确的MC推荐。具体而言，首先通过MC用户扩展图构建物品-物品相似度图；其次设计CA-GF模型，其核心组件包括：1）准则特定图滤波——利用多种多项式低通滤波器为每个准则寻找最优滤波器，2）准则偏好融合聚合——对各准则平滑后的信号进行集成。实验表明CA-GF具有以下特性：（a）高效性：在最大基准数据集上运行时间低于0.2秒，具备卓越计算效率；（b）准确性：超越现有MC推荐基准方法，与最佳竞争模型相比最高可获得24%的准确率提升；（c）可解释性：通过可视化技术为各准则对模型预测的贡献提供解释依据。