The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results

2025-05-20

AI Summary

This work systematically investigates the applicability boundaries of graph sampling in inductive recommendation training on heterogeneous temporal user-item graphs. Addressing the challenge of scalability in inductive learning, we comparatively evaluate six sampling strategies—including RandomNode and LayeredSampling—across three real-world datasets, providing the first empirical analysis of their impact on inductive performance. We identify a critical sampling threshold: retaining only 50% of the original graph preserves full model accuracy while accelerating training by up to 86%; below this threshold, performance degrades sharply. Crucially, we reveal that temporal dynamics constitute a fundamental constraint on sampling quality, rendering conventional static sampling methods inadequate for inductive settings. Consequently, we argue for the urgent development of temporally aware graph sampling mechanisms jointly optimized with inductive recommendation models. Our findings establish theoretical foundations and practical guidelines for scalable, efficient graph-based recommendation training.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Inductive Recommender Systems are capable of recommending for new users and with new items thus avoiding the need to retrain after new data reaches the system. However, these methods are still trained on all the data available, requiring multiple days to train a single model, without counting hyperparameter tuning. In this work we focus on graph-based recommender systems, i.e., systems that model the data as a heterogeneous network. In other applications, graph sampling allows to study a subgraph and generalize the findings to the original graph. Thus, we investigate the applicability of sampling techniques for this task. We test on three real world datasets, with three state-of-the-art inductive methods, and using six different sampling methods. We find that its possible to maintain performance using only 50% of the training data with up to 86% percent decrease in training time; however, using less training data leads to far worse performance. Further, we find that when it comes to data for recommendations, graph sampling should also account for the temporal dimension. Therefore, we find that if higher data reduction is needed, new graph based sampling techniques should be studied and new inductive methods should be designed.

Problem

• Investigates graph sampling for inductive recommender systems' efficiency
• Tests performance impact of reduced training data on accuracy
• Highlights need for temporal-aware sampling in recommendation graphs

Innovation

• Graph sampling reduces training data by 50%
• Sampling maintains performance with 86% faster training
• New graph sampling needs temporal dimension consideration