Intelligent Recommender Systems for Chinese Super League Fan Consumption Behavior Prediction

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.