/pop-the-corn

Movie recommendation website based on user preferences and film similarity (actors, genres, descriptions).

Primary LanguageScala

Pop The Corn

Description

Side-Project to apply what I learned

.
├── api
├── data
├── front
├── indexer
├── libraries
└── mlapi

Data

Content-Based Recommendation

  • Natural Language Processing (NLP)
    • TF-IDF
    • Latent Dirichlet Allocation (LDA)
  • Users Preferences
    • Features to consider : Genres, Actors, Directors, etc ... (For the moment I only use Genre)
    • Based on the user ratings of several movie profiles, we establish a user profile
    • Cosine Similarity to find the similarity between user profile and movie profile

Collaborative Recommendation

  • ... Not enough data yet ...

Backend

Enriching and Indexing in ES with Akka Actors And Spark

  • Akka Actor System : restriction : 40 queries per second allowed by external API
    • System based on a supervisor sending batch of movies to some workers to enrich the movie and indexing it
  • Spark : Creating autocomplete index from movie titles in ES to ES
    • Backpressure with .coalesce(20)
  • GraphQL : Use to query the machine learning API with Sangria

Front

React/Redux

portfolio-popthecorn