Build a production-ready movie/product recommendation system using collaborative filtering, content-based filtering, and hybrid approaches.
Build a production-ready movie/product recommendation system using collaborative filtering, content-based filtering, and hybrid approaches. This comprehensive guide covers everything from design through implementation, testing, and deployment.
Compute user-user similarity matrix: cosine_similarity(user_item_matrix). For target user, find K most similar users (neighbors). Predict rating for unrated items: weighted average of neighbor ratings. Challenges: cold start (new users/items), sparsity (most users rate few items), scalability (computing similarity for 1M users is expensive). UserKNN with K=20 neighbors achieves RMSE ≈ 0.95 on MovieLens 100K.
10 components required for this project.
| # | Component | Purpose | Qty |
|---|---|---|---|
| 1 | Python 3.10+ | Main language | x1 |
| 2 | Pandas + NumPy | Data manipulation | x1 |
| 3 | scikit-learn | Cosine similarity, SVD | x1 |
| 4 | Surprise library | Collaborative filtering (SVD, KNN) | x1 |
| 5 | LightFM | Hybrid recommendation | x1 |
| 6 | Flask + SQLAlchemy | REST API and database | x1 |
| 7 | Redis | Recommendation caching | x1 |
| 8 | MLflow | Experiment tracking | x1 |
| 9 | MovieLens dataset | 100K ratings training dataset | x1 |
| 10 | Apache Spark (optional) | Distributed ALS for scale | x1 |
Follow these 5 steps carefully.
Compute user-user similarity matrix: cosine_similarity(user_item_matrix). For target user, find K most similar users (neighbors). Predict rating for unrated items: weighted average of neighbor ratings. Challenges: cold start (new users/items), sparsity (most users rate few items), scalability (computing similarity for 1M users is expensive). UserKNN with K=20 neighbors achieves RMSE ≈ 0.95 on MovieLens 100K.
Decompose user-item matrix R into R ≈ U × Σ × Vᵀ. U = user latent factors (user preferences in K-dimensional space). V = item latent factors (item characteristics in K-dimensional space). Prediction: r̂(u,i) = U[u] · V[i]ᵀ. Train using SGD: for each known rating r(u,i), update U[u] and V[i] to minimize (r(u,i) - r̂(u,i))². Add L2 regularization. SVD (k=100 latent factors) achieves RMSE ≈ 0.87 on MovieLens 100K — significantly better than UserKNN.
Create item feature vectors from metadata: for movies — genre (one-hot encoded), director, cast (TF-IDF), release year, IMDB score. Compute item-item cosine similarity matrix from features. Recommend: find movies similar to what user has highly rated. Advantages: no cold start for items (new movie with known features gets recommendations immediately), no need for other users
Combine collaborative and content-based predictions: hybrid_score = α × CF_score + (1-α) × CB_score. Tune α on validation set. LightFM implements hybrid directly: learns user/item latent representations incorporating both interaction data and item/user features. More advanced: stacking — train CF and CB as base models, train a meta-learner (gradient boosting) on their predictions. Achieves significantly better performance than any single approach.
Offline metrics (RMSE, Precision@K, Recall@K, NDCG) are necessary but not sufficient — a model with lower RMSE may perform worse in production. A/B test: randomly assign users to control (current system) and treatment (new model). Measure: click-through rate (CTR), conversion rate, session length, items added to cart. Implement feature flags to gradually roll out new model (5% → 20% → 50% → 100%) monitoring for performance regressions.
Core code for recommender.py:
Test Recommendation System by verifying each subsystem individually before full integration.
Verify power voltages, check ground connections, use serial monitor for debug.
An interactive simulator will be available here — simulate circuits and run code in-browser without hardware.