Building Recommender System in PHP8 | Mihailo Joksimovic

Key Takeaways

• Recommendation systems are often based on incomplete matrices, with only some input data.
• Dimensionality reduction is a process that helps to reduce the number of dimensions in a dataset, making it easier to understand and work with.
• Cosine distance is a simple method to compare two vectors.
• KNN (K-nearest neighbors) algorithm is a type of collaborative filtering that can be used to recommend items to users.
• In machine learning, vectors are important because everything boils down to pattern matching and using vectors as inputs to black boxes.
• Recommendation systems have been a niche of machine learning that has been developed over the years.
• PHP has two great libraries, RubiXML and others, that can be used to build recommendation systems.
• The KNN algorithm boils down to measuring how similar two vectors are and then using those similarities to make predictions.
• Collaborative filtering is a method that focuses on the content, rather than the user, to make recommendations.
• The concept of neighbors is important in recommendation systems, as it helps to identify similar users or items.
• Sparse matrices are used to store recommendation systems data, as they can be faster and more efficient.
• The cosine similarity is a useful metric for measuring the similarity between two vectors.
• PHP 8 has a concept of foreign function interface (FFI) that can be used to improve performance and recommendation results.
• Recommendation systems can be used in various fields, such as movies, books, music, and more.
• In the context of recommendation systems, dimensionality reduction is used to reduce the number of dimensions in a dataset, making it easier to understand and work with.