Enhancing Collaborative Filtering via Learned User Graphs

1. Improving Collaborative Filtering Recommendation
via
Graph Learning
• A Deep Dive into Graph Learning
• Paper by Yongyu Wang, JD Logistics
• https://arxiv.org/abs/2311.03316
Neeharika Singh

2. Introduction
• Recommendation systems in e-commerce.
• Predicts preferences using historical data.
• Navigates vast product selections efficiently.
• Importance of personalized user experience.
• Enhances customer satisfaction
• Purchase decision
• Role of Collaborative Filtering (CF) in recommendations.
• Predicts based on user similarities.
• Enhances accuracy through shared preferences.

3. Understanding Collaborative Filtering
• Basic principle: Users with similar past preferences likely have similar future preferences.
• CF can be classified in two categories
• User Based CF
• Item Based DF
• Traditional method
• k-nearest neighbor (k-NN) graph approach.

4. Limitations of Traditional k-NN in CF
• Fixed-Size Neighborhood
• Limits considering a broader pattern in data.
• Local Relationships
• Focuses mainly on local preferences, missing global context.

5. Graph Learning in Collaborative Filtering
• Innovative integration of Graph Learning into CF.
• Aims to enhance recommendation accuracy and efficiency.
• Addresses limitations of traditional k-NN approach.

6. Graph Learning in Collaborative Filtering
• Graph Learning Concept
• Users and items as graph nodes, with edges symbolizing their interactions.
• Graphs' Role in CF
• Provide intricate visualization and understanding of complex user-item relationships.
• Learning Graph Dynamics
• Involves sophisticated data-driven optimization to define meaningful connections.

7. Enhancing Graph Construction in Collaborative Filtering
• Initial Base Graph Construction
• Start with a simple k-NN graph using a minimal 'k' (e.g., k = 2) to limit redundant connections.
• Graph Learning Optimization
• Employ an optimization model to refine the graph, ensuring smooth signals and controlled sparsity.
• Topology-Based Prediction Method
• Use the refined graph for prediction in user-based and item-based CF, focusing on weighted sums from
connected nodes or items.

8. Experimental Validation and Results
• Use of MovieLens dataset for validation.
• Evaluation metrics : mean absolute error (MAE) and root mean square error (RMSE)
• Graph learning approach outperforms k-NN in accuracy.
• Maintains superior performance with complex and sparse graph.

9. Conclusion
• Impact on online shopping experience.
• Significance in the field of machine learning and AI.
• Future potential of graph learning in e-commerce.