Movie Lens Recommender

This is a class project developed for a course from Simon Fraser University's master of professional computer science program. Like the original Movie Lens system developed by a research lab in the Department of Computer Science and Engineering at the University of Minnesota, this is a personalized recommender system aiming to deliver good UX when navigating through a large repository of contents (movies/videos).

Problem definition

• The scale of the content repository: 10^6.
• The number of active users in the system: 10^7.
• Must achieve high precision.
• Under resonable cost, 95th percentile latency should be kept below 1 second.

Datasets Reference & Resources

• https://grouplens.org/datasets/movielens/latest
• https://towardsdatascience.com/movie-recommendation-system-based-on-movielens-ef0df580cd0e
• 
  • This product uses the TMDB API but is not endorsed or certified by TMDB.

Preparations

1. third_party/README.md
2. src/ingestion/README.md
3. src/loader/README.md
4. src/feature_processor/README.md
5. src/model/README.md

Author Mapping

• DaviesX (Chifeng Wen) -> cwa258 (chifeng_wen@sfu.ca)
• ffmm6521 (Min Fei) -> mfa94 (??@sfu.ca)
• greedcat (Mingyi Wu) -> mingyiwu (mingyiwu@sfu.ca)