Project

A project shows you can go end-to-end: clean interaction data, build a recommender, evaluate it properly, and deliver measurable improvements. It becomes a strong portfolio artifact because it mirrors exactly what industry teams build and iterate on.

Getting Started

List of Probable Project Topics

  • Movie Recommendation Engine (collaborative filtering + top-K evaluation on MovieLens)
  • E-commerce Product Recommender (co-purchase + ratings + popularity baselines)
  • Music/Playlist Recommender (artist/track similarity + user history)
  • News/Article Recommender (freshness-aware ranking + category diversity)
  • Book Recommender (content + collaborative hybrid using metadata and reviews)
  • Restaurant Recommender (ratings + location + time-of-day context)
  • Job Recommendation System (skills/titles similarity + user profile matching)
  • Course Recommender (student interest + completion patterns + prerequisites graph)
  • Session-Based Recommender (next-click prediction from browsing sessions)
  • Cold-Start Recommender (use item/user metadata to handle new items/users)
  • Hybrid Recommender (blend CF + content-based + popularity priors)
  • Explainable Recommender (“recommended because…” using tags/features)
  • Diversity & Novelty Re-ranking (increase coverage/long-tail while keeping relevance)
  • Implicit Feedback Recommender (views/clicks with ALS/BPR-style ranking)
  • Graph-Based Recommendations (random-walk / item-item graph similarity)
  • A/B Testing Simulation + Offline Metrics (CTR proxy, HR@K, NDCG@K, MAP)
  • Bias & Fairness Audit for Recsys (popularity bias, exposure imbalance)
  • Recommendation API + Mini App (serve recommendations with a simple UI)