DPC2022: Building Recommender System in PHP8


Learn how to build a recommender system in PHP8 using real-world examples, PHP libraries and hands-on demonstrations, and explore the power of dimensionality reduction and vector representation in accurate predictions.

Key takeaways
  • Recommendation systems are based on the concept of dimensionality reduction, where vectorized data is reduced to a lower dimension to uncover hidden patterns and enable accurate predictions.
  • The most common recommendation algorithm is Collaborative Filtering, which involves finding similarities between users or items.
  • Content-Based Filtering is another approach, which recommends items with similar attributes to those a user has liked or interacted with.
  • To build a recommendation system, you need to start with a data set, such as the MovieLens dataset, which contains user ratings and movie metadata.
  • Vectors are used to represent users or items, and dimensionality reduction techniques like PCA (Principal Component Analysis) or SVD (Singular Value Decomposition) are used to reduce the dimensionality of the data.
  • Cosine distance and K-Nearest Neighbors (KNN) are used to measure the similarity between vectors and find the most similar users or items.
  • PHP libraries like PHPML and RubiXML can be used to build recommendation systems in PHP.
  • The Netflix original recommendation system engine used a combination of methods, including content-based filtering, collaborative filtering, and dimensionality reduction.
  • Starting a recommendation system project in PHP is easier than it seems, and you can get started with a simple script that uses KNN to recommend items.
  • The presentation highlighted the importance of understanding vector representation and dimensionality reduction in building a recommendation system.