Recommendation System

This roadmap is designed to take you from fundamentals to production-level understanding of recommendation systems — the kind of mastery expected in Top Tech Company Interviews.
You’ll learn not only how models work but also why design choices matter at scale — in real-world systems that power billions of recommendations daily.

🤖 Core ML Fundamentals

1.1: Understand the Essence of Predictive Modeling.

1. Review regression and classification basics — how models map inputs to outputs.
2. Learn how these foundations translate to recommendations: e.g., predicting a user’s rating (regression) or whether they’ll click (classification).
3. Understand how bias-variance tradeoff manifests in recommendation settings.

Deeper Insight: Expect to discuss how traditional ML models (like logistic regression or gradient boosting) can serve as the baseline recommenders — and how they differ from collaborative or deep models in scalability and personalization.

1.2: Revisit Core Optimization and Evaluation.

1. Review MSE, MAE, and RMSE as loss functions in rating prediction.
2. Study ranking metrics: Precision@K, Recall@K, MAP, and NDCG.
3. Explore train-test splits for temporal data (e.g., by timestamp) — crucial for recommender evaluations.

Probing Question: “Why might RMSE be misleading for recommendation systems?”
Understand that a low RMSE doesn’t guarantee good ranking — which is what matters most in practice.

🧩 Classical Recommendation Algorithms

2.1: User-User and Item-Item Collaborative Filtering.

1. Learn the concept of similarity (cosine, Pearson, Jaccard).
2. Implement user-based CF and item-based CF using rating matrices.
3. Explore KNN approaches and their scalability trade-offs.

Deeper Insight: Be prepared to discuss how to handle sparse matrices efficiently (e.g., scipy.sparse).
Probing Question: “What happens when most users rate very few items?” Discuss techniques like dimensionality reduction or hybrid methods.

2.2: Matrix Factorization.

1. Derive the objective:
   \[ \min_{P, Q} \sum_{(u,i) \in \mathcal{D}} (r_{ui} - P_u^T Q_i)^2 + \lambda(||P_u||^2 + ||Q_i||^2) \]
2. Learn optimization using Stochastic Gradient Descent (SGD).
3. Implement a basic MF model in Python using NumPy.
4. Discuss hyperparameters (λ, latent dimensions).

Probing Question: “Why do we regularize both user and item vectors?”
Understand the trade-off between overfitting and underfitting in latent spaces.

2.3: Singular Value Decomposition (SVD) & Implicit Feedback.

1. Study how SVD decomposes the rating matrix into latent factors.
2. Understand the difference between explicit (ratings) and implicit (clicks, views) feedback.
3. Explore Alternating Least Squares (ALS) for large-scale implicit feedback datasets.

Deeper Insight: Discuss the scalability of ALS on Spark and how distributed computation supports real-world systems.

🧠 Deep Learning for Recommendations

3.1: Learn Neural Collaborative Filtering (NCF).

1. Understand the concept of embeddings for users and items.
2. Derive how dot-product similarity becomes a learnable neural function.
3. Implement a simple NCF in PyTorch/Keras.
4. Analyze how this compares with Matrix Factorization.

Probing Question: “Why might learned embeddings outperform static similarity metrics?”
Discuss representation learning and generalization over sparse interactions.

3.2: DeepFM, Wide & Deep, and AutoRec.

1. Understand the motivation for combining memorization (wide) and generalization (deep).
2. Learn how DeepFM models feature interactions using Factorization Machines + DNN.
3. Explore AutoRec — a neural autoencoder approach to collaborative filtering.

Deeper Insight: Be prepared to discuss feature crosses, embedding sharing, and cold-start mitigation using metadata features.

3.3: Sequential and Contextual Models.

1. Learn how RNNs, GRUs, or Transformers model user behavior sequences.
2. Study architectures like SASRec and BERT4Rec.
3. Explore time-based context and session-level modeling.

Probing Question: “How does a sequence model handle the long-tail problem?”
Discuss attention mechanisms and dynamic user intent modeling.

🧮 Feature Engineering & Data Handling

4.1: Handle Sparse & Imbalanced Data.

1. Learn techniques for encoding user/item interactions.
2. Study negative sampling and its importance for implicit feedback.
3. Implement mini-batch sampling for balanced training.

Probing Question: “Why not train on all user-item pairs?”
Discuss computational cost, class imbalance, and signal dilution.

4.2: Contextual and Hybrid Features.

1. Combine collaborative and content-based signals.
2. Engineer session-level, time-based, or demographic features.
3. Use embeddings for categorical features and scaling tricks for dense features.

Deeper Insight: Discuss trade-offs between interpretability (content-based) and accuracy (collaborative).

⚙️ System Design & MLOps for Recommenders

5.1: Real-Time vs. Batch Recommendations.

1. Understand candidate generation, scoring, and ranking phases.
2. Study approximate nearest neighbor (ANN) search using FAISS or ScaNN.
3. Compare offline batch training with online incremental learning.

Probing Question: “How would you serve recommendations in under 50 ms?”
Discuss caching, precomputation, and approximate retrieval.

5.2: Model Deployment and Monitoring.

1. Learn how to deploy recommendation models as APIs.
2. Set up A/B testing pipelines to measure user engagement metrics (CTR, dwell time).
3. Monitor drift, freshness decay, and feedback loops.

Deeper Insight: Understand how feedback loops can cause popularity bias — and how to mitigate with exploration strategies.

5.3: Evaluation at Scale.

1. Design large-scale evaluation frameworks using distributed computing (Spark, Ray).
2. Track both offline metrics (Precision@K) and online metrics (CTR uplift).
3. Learn about diversity, novelty, and fairness metrics.

Probing Question: “If your CTR improves but user retention drops, what might be happening?”
Be ready to discuss short-term engagement vs. long-term satisfaction trade-offs.

🧭 Advanced Topics & Research Directions

6.1: Graph-Based Recommendations.

1. Understand how users and items form a bipartite graph.
2. Learn GNN-based methods (GraphSAGE, PinSage) for recommendation.
3. Explore message passing and embedding propagation.

Probing Question: “How does graph structure help in cold-start?”
Discuss leveraging neighborhood information for inductive reasoning.

6.2: Reinforcement Learning in Recommenders.

1. Frame recommendation as a sequential decision problem.
2. Learn about bandit algorithms (ε-greedy, UCB, Thompson Sampling).
3. Explore policy-gradient based methods for long-term user engagement.

Deeper Insight: Be prepared to reason about exploration vs. exploitation and delayed reward optimization.

6.3: Causal Inference & Debiasing.

1. Understand exposure bias and selection bias in logged data.
2. Study counterfactual learning-to-rank techniques (IPS, SNIPS).
3. Explore causal models for unbiased recommendation.

Probing Question: “Why might your recommender reinforce its own bias?”
Discuss how over-optimization on historical data can lead to feedback loops.