Models

LightFM

• A hybrid approach that has adv from both collaborative and content-based filtering.
• Flexibile: Can incorporates metadata such as user preferences or item attributes.
• Scalable: Ok for large dataset (millions of users and items) (Python implementation). Optimized for sparse data.
• Customizable: Can use multiple loss functions:
  • Bayesian Personalized Ranking (BPR)
  • Weighted Approximate-Rank Pairwise (WARP)
• Interpretable: Clear separation between user/item embeddings –> help debugging and understanding.
• Industry examples:
  • Ecommerce: Personalized product recommendations (e.g., Amazon, Flipkart).
  • Streaming Platforms: Movie, music, content suggestions (e.g., Netflix, Spotify).
  • EdTech: Course or book recommendations tailored to users.
  • Healthcare: Personalized wellness plans or medication suggestions.
  • Hospitality: Travel or hotel recommendations based on user profiles.
• Ref:
  • LightFM in Action: AI Recommendations with Python.
  • Building a Recommendation System with LightFM
  • Recommendation System in Python: LightFM (Step-by-step guide)

References

• Ecommerce-Recommender-System-On-Aws-With-Mlops