Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation

2025-02-13

AI Summary

Addressing the challenge of balancing model accuracy and training efficiency in multi-criteria (MC) recommendation, this paper proposes a training-free, criterion-aware graph filtering framework. First, a multi-criterion user expansion graph is constructed to capture cross-criterion similarity. Second, optimal low-pass graph filters are independently designed for each criterion to enable criterion-specific signal smoothing. Finally, preference-weighted aggregation yields the final recommendation. This work introduces the first parameter-free graph filtering paradigm for MC recommendation, eliminating the need for gradient-based optimization or iterative training. The framework achieves millisecond-scale inference latency (<0.2 s), delivers substantial accuracy gains—up to +24% in NDCG—compared to state-of-the-art methods, and provides interpretable, criterion-wise contribution visualization. Evaluated on the largest publicly available MC recommendation benchmark, it consistently outperforms all existing approaches across multiple metrics.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Efficient multi-criteria recommendation method
• Training-free graph filtering approach
• Scalable and accurate MC recommendation

Innovation

• training-free MC recommendation
• criteria-aware graph filtering
• polynomial low-pass filters