DPC2022: Building Recommender System in PHP8


Discover how to build a recommendation system in PHP8, using techniques like K-nearest neighbors, cosine similarity, and dimensionality reduction. Explore content-based and collaborative filtering approaches, and learn from the Netflix Prize competition.

Key takeaways
  • Recommendation systems are a niche area of research and development.
  • Netflix’s original recommendation system engine was based on data classification.
  • K-nearest neighbors (KNN) is a simple and effective algorithm for building recommendation systems.
  • Cosine similarity is a common metric for comparing vectors.
  • Dimensionality reduction algorithms can be used to convert sparse matrices into fully specified matrices.
  • Content-based filtering and collaborative filtering are two main approaches to building recommendation systems.
  • PHP has two great libraries for machine learning: RubiXML and PHPML.
  • The Netflix Prize was a competition that challenged participants to build a better recommendation system than Netflix’s existing system.
  • The winning algorithm used a combination of content-based filtering and collaborative filtering.
  • Recommendation systems are becoming increasingly important as more and more data becomes available.