Building Recommender System in PHP8 | Mihailo Joksimovic


Discover how to build powerful recommender systems using PHP8. Explore vector representations, cosine similarity, KNN, collaborative filtering, content-based filtering, dimensionality reduction, and matrix factorization.

Key takeaways
  1. Recommendation systems are a niche of data classification, where the goal is to find similar users or items based on their features.
  2. Vectors are ordered lists of numbers that represent data points, and they are the building blocks of recommendation systems.
  3. Cosine similarity is a metric used to measure the similarity between two vectors, and it is commonly used in recommendation systems.
  4. K-nearest neighbors (KNN) is a simple and effective algorithm for finding similar users or items based on their cosine similarity.
  5. Collaborative filtering is a type of recommendation system that relies on the ratings of other users to make recommendations.
  6. Content-based filtering is a type of recommendation system that uses the features of items to make recommendations.
  7. Principal component analysis (PCA) and singular value decomposition (SVD) are dimensionality reduction techniques that can be used to reduce the number of features in a dataset.
  8. Matrix factorization is a technique used in recommendation systems to find hidden patterns in the data.
  9. PHP has two popular libraries for machine learning: RubiXML and PHPML.
  10. Training a machine learning model involves making predictions, evaluating the accuracy of those predictions, and adjusting the model’s parameters to improve accuracy.