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Beyond single perspective bias: Fusing personalized and common preferences for comprehensive personal preference
JiaXin Wu1, Guangxiong Chen1, Chenglong Pang2
1Department of Data Science and Business Intelligence, School of Management, Guangdong University of Technology, Guangzhou, China.
Summary
This study introduces P&CGCN, a novel recommendation system that uniquely fuses personalized and common user preferences. It improves accuracy by adaptively balancing these preferences, outperforming existing models.
Area of Science:
- Artificial Intelligence
- Computer Science
- Data Science
Background:
- Graph Convolutional Network (GCN)-based recommendation systems often exhibit bias towards popular items, neglecting personalized user preferences.
- Existing methods that suppress popular item information may overlook valuable common preference signals, leading to another form of bias.
Purpose of the Study:
- To propose P&CGCN, a unified framework that collaboratively fuses personalized and common user preferences in recommendation systems.
- To address the limitations of existing GCN models by avoiding single-perspective biases.
Main Methods:
- Developed a unified framework with intra-layer aggregation and inter-layer combination.
- Introduced P&C degree for adaptive quantification of personal and common preferences within layers.
- Designed P&C depth to prioritize shallow personalized and deep common preference signals across layers.
Main Results:
- P&CGCN demonstrates superior performance and efficiency compared to existing methods on four real-world datasets.
- Achieved approximately 20% performance improvement over LightGCN on sparse, large-scale datasets.
- Showcased at least a 2x speedup in training efficiency.
Conclusions:
- P&CGCN effectively balances personalized and common preferences, leading to more accurate and efficient recommendations.
- The proposed P&C degree and P&C depth mechanisms are key to mitigating bias in GCN-based recommenders.
- This approach offers a significant advancement for recommendation systems, particularly in handling data sparsity.
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