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Title
Commerce Product Recommendation System - Enhanced Collaborative Filtering and Model Evaluation
Enhancement Aim
to improve the recommendation system's effectiveness by adding and refining collaborative filtering methods, optimizing model performance, and providing a more comprehensive evaluation metric. This will ensure more personalized and accurate recommendations for users, which is expected to enhance user satisfaction and drive more sales for e-commerce businesses.
Changes
xpand Similarity-Based Collaborative Filtering:
Implement additional similarity measures (e.g., Pearson correlation) to explore their effects on recommendation quality. Optimize the similarity search function for efficiency and scalability. Refine Model-Based Collaborative Filtering:
Increase the number of latent features in the SVD model to capture more nuanced user-product interactions. Experiment with alternative matrix factorization methods, such as Non-negative Matrix Factorization (NMF) for potentially improved performance. Model Evaluation Enhancements:
Implement additional metrics such as Mean Absolute Error (MAE) and Precision@K to provide a more rounded evaluation. Add visualizations of model performance across different metrics to aid in interpretability.
Screenshots 📷
No response
Guidelines
Full Name
Nimra Aslam
Participant Role
GSSOC, GSSOC Extended