Project

This course teaches you how platforms like Netflix, Amazon, and Spotify personalize experiences at scale, turning user behavior into relevant, timely recommendations. Deep learning takes you beyond basic collaborative filtering to handle complex signals, cold-start challenges, and rich content (text/images) for smarter personalization. A project will demonstrate that you can build a full recommender pipeline, data prep, model training, ranking, and evaluation, on real interaction data. It becomes a standout portfolio piece because recommendation systems are one of the most in-demand, high-impact applied ML skills in industry.

Getting Started

List of Probable Project Topics

  • Movie Recommender (Neural CF) using MovieLens with ranking metrics (NDCG/HR@K).
  • Two-Tower Retrieval Model (user tower + item tower) for large-scale candidate generation.
  • Learning-to-Rank Recommender (DeepFM / Wide&Deep / xDeepFM) for CTR-style prediction.
  • Session-Based Recommendations (GRU4Rec / Transformers) for next-item prediction in clickstreams.
  • Sequential Transformer Recommender (SASRec/BERT4Rec-style) for long-term user behavior modeling.
  • Cold-Start Recommender using item metadata (genres, tags, text descriptions) with embeddings.
  • Content-Based Product Recommender using product descriptions + review text (BERT embeddings + ranking).
  • News/Article Recommender with recency-aware ranking and diversity constraints.
  • Music Playlist Continuation using sequence models and embedding similarity.
  • Fashion Recommender (Multimodal) using image embeddings + user interactions.
  • Diversity- & Fairness-Aware Recommender (re-ranking for coverage, novelty, long-tail support).
  • Explainable Recommender that generates “because you liked…” reasons using attention/features.
  • Online Evaluation Simulator (A/B testing mock + counterfactual evaluation with IPS).
  • Graph Neural Network Recommender (LightGCN/PinSage-style) on a user–item bipartite graph.
  • Hybrid Recommender combining collaborative + content + popularity priors with ensembling.
  • Incremental/Streaming Updates (daily retraining + embedding refresh + drift tracking).
  • Bundle/Complementary Item Recommender (cart-aware suggestions, co-purchase mining + deep ranking).
  • Restaurant/Food Recommender using ratings + location + time-of-day context (context-aware recsys).