Intelligent Recommender Systems for Chinese Super League Fan Consumption Behavior Prediction

Summary

This study examines whether relationship-marketing factors improve sports recommender systems for Chinese Super League fans. Incorporating satisfaction, trust, commitment, and reciprocity significantly enhances Top-N recommendation accuracy, especially under sparse and long-tail conditions. Results show that commitment and reciprocity drive repeated high-involvement behavior, while satisfaction and trust mainly support early engagement.

Abstract

Understanding and predicting fan consumption behavior is a central challenge in professional sports, yet most recommender systems rely primarily on historical interactions and pay limited attention to relationship-marketing factors that shape fan engagement. To address this gap, this study develops MLP-PA (Multi-Layer Perceptron with Pyramid Attention) and examines whether incorporating fans’ relational states—such as satisfaction, trust, commitment, and reciprocity—can improve recommendation effectiveness across multiple consumption scenarios among Chinese Super League (CSL) fans. Using a multi-scenario dataset covering ticketing, merchandise, membership, and digital content, empirical results show that recommendations generated by MLP-PA achieve significantly higher Top-N ranking accuracy than conventional collaborative filtering approaches, with particularly pronounced improvements in sparse and long-tail settings. The analysis further reveals that different relationship factors are associated with distinct consumption patterns: commitment and reciprocity are more strongly linked to repeated and high-involvement behaviors, whereas satisfaction and trust mainly influence initial and short-term responses. These findings demonstrate that relationship-marketing factors play a substantive and differentiated role in fan consumption behavior, offering actionable insights for personalized engagement strategies and digital operations in professional football.

Introduction

In recent years, recommender systems have become a central component of digital platforms, enabling the alignment of users, products, and consumption contexts through data-driven personalization. While recommender technologies are well established in domains such as e-commerce and online media, their application in professional sports remains comparatively underdeveloped¹. In leagues such as the Chinese Super League (CSL), fan consumption behavior is highly context-sensitive and evolves across pre-match, match-day, and post-match phases, with purchasing decisions closely intertwined with offline attendance, emotional attachment, and social iden....

Protocol

This study uses de-identified fan–item interaction logs and anonymized questionnaire data collected for research purposes only. No personally identifiable information was accessed, stored, or processed. According to institutional and national guidelines, this study did not require formal ethics committee approval or individual informed consent.

Dataset preparation and experimental procedure
Step 1. Data preparation and preprocessing
Dataset preparation and experimental procedures were designed to transform raw behavioral logs and survey responses into a unified, reproducible experimental dataset sui....

Results

Convergence and stability

To evaluate the convergence behavior and training stability of the proposed model, we compare its validation performance against a standard MLP-based recommender without pyramid feature attention or relationship-aware attention, which serves as the control model. All models are trained under identical data splits, optimization settings, and stopping criteria to ensure a fair comparison.

Discussion

Conceptual and methodological contributions

This study contributes conceptually by reframing relationship dimensions—satisfaction, trust, commitment, and reciprocity—not as static auxiliary features, but as context-sensitive relational signals whose influence varies across engagement stages. The proposed framework operationalizes this idea through an attention-based mechanism that dynamically reweights relational cues in conjunction with behavioral representations........

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
	Python	https://www.python.org	3.9.10
Deep learning framework	PyTorch	https://pytorch.org	>= 1.12
Hyperparameter optimization library	Optuna	https://optuna.org	>= 3.0
Hardware	NVIDIA GPU	https://www.nvidia.com	Single GPU, >= 8 GB memory
Dataset	Self-built CSL fan dataset	N/A	One full season or longer; scale sufficient for deep modeling
Interaction data	Fan–item implicit interaction logs	N/A	Not separately versioned
Survey data	Relationship questionnaire data	N/A	Not separately versioned
Data preprocessing method	Hash-based anonymization	N/A	Applied to fan and item identifiers
Data preprocessing method	Mean imputation within membership tier	N/A	Applied to missing questionnaire responses
Data preprocessing method	Min–max normalization	N/A	Range [0, 1]
Data splitting strategy	Chronological user-level split	N/A	Most recent = test; second most recent = validation; remainder = training
Negative sampling	Uniform negative sampling	N/A	Positive:negative ratio = 1:5
Model backbone	MLP (Multi-Layer Perceptron)	https://pytorch.org/docs/stable/nn.html	Hidden layers [128, 64, 32] (optimized)
Attention module	Relationship-attention channel	N/A	Custom module
Fusion module	Pyramid Attention (PA)	N/A	Pooling scales {1, 2, 4}, stride = kernel size, reduction ratio = 4
Optimizer	Adam	https://pytorch.org/docs/stable/generated/torch.optim.Adam.html	Learning rate 0.001, weight decay 1e-5
Regularization	Dropout	https://pytorch.org/docs/stable/generated/torch.nn.Dropout.html	Rate 0.3