A Reproducible and Fair Evaluation of Partition-aware Collaborative Filtering

2025-12-18

AI Summary

This paper addresses inconsistencies, data opacity, and missing baselines in evaluating partition-aware collaborative filtering (CF) models (e.g., FPSR/FPSR+), establishing the first fair and reproducible unified benchmark. Methodologically, it introduces a standardized data partitioning protocol, integrates subgraph-local similarity modeling with fine-grained similarity refinement, and open-sources a full-stack evaluation framework. Contributions include: (1) the first fair and reproducible evaluation of partition-aware CF; (2) empirical identification of an inherent accuracy–coverage trade-off induced by partitioning strategies; and (3) demonstration that FPSR variants significantly outperform mainstream baselines in long-tail scenarios, with their global component design proving critical to performance. Results provide empirical foundations and scalable guidance for partition-aware recommendation systems.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Similarity-based collaborative filtering (CF) models have long demonstrated strong offline performance and conceptual simplicity. However, their scalability is limited by the quadratic cost of maintaining dense item-item similarity matrices. Partitioning-based paradigms have recently emerged as an effective strategy for balancing effectiveness and efficiency, enabling models to learn local similarities within coherent subgraphs while maintaining a limited global context. In this work, we focus on the Fine-tuning Partition-aware Similarity Refinement (FPSR) framework, a prominent representative of this family, as well as its extension, FPSR+. Reproducible evaluation of partition-aware collaborative filtering remains challenging, as prior FPSR/FPSR+ reports often rely on splits of unclear provenance and omit some similarity-based baselines, thereby complicating fair comparison. We present a transparent, fully reproducible benchmark of FPSR and FPSR+. Based on our results, the family of FPSR models does not consistently perform at the highest level. Overall, it remains competitive, validates its design choices, and shows significant advantages in long-tail scenarios. This highlights the accuracy-coverage trade-offs resulting from partitioning, global components, and hub design. Our investigation clarifies when partition-aware similarity modeling is most beneficial and offers actionable guidance for scalable recommender system design under reproducible protocols.

Problem

• Reproducible evaluation of partition-aware collaborative filtering models
• Fair comparison of FPSR/FPSR+ with similarity-based baselines
• Clarifying accuracy-coverage trade-offs in scalable recommender systems

Innovation

• Partition-aware similarity modeling for scalable collaborative filtering
• Fine-tuning Partition-aware Similarity Refinement (FPSR) framework
• Reproducible benchmark evaluating partitioning and global components