Recommendation System Using Online Node2Vec With Memgraph MAGE

The blog post discusses the implementation of an Online Recommendation System using the newly integrated Online Node2Vec algorithm in Memgraph's MAGE library. This algorithm is designed to update temporal node embeddings dynamically, allowing for real-time tracking and measurement of node similarity within graph streams. The tutorial guides users through setting up Memgraph and MAGE, configuring the Online Node2Vec algorithm using Memgraph Triggers, and using k-means clustering from the scikit-learn package to cluster data based on node embeddings. The tutorial utilizes a dataset from the high-energy physics citation network to demonstrate how the algorithm can identify similar papers, achieving this through temporal walks and embeddings optimized by the Word2Vec learner. The process underscores the algorithm's ability to handle event-driven data, offering potential applications in various domains where real-time graph analysis is crucial. MAGE, an open-source library with extensive graph algorithms, is highlighted as a robust tool for analyzing and visualizing graph networks, with the blog encouraging contributions to its repository.