Modern machine learning demands new approaches. A powerful ML workflow requires more than picking the right algorithms. You also need the right tools, technology, datasets and model to brew your secret ingredient: context.

In his book, Graph-Powered Machine Learning, Dr. Alessandro Negro explores the new way of applying graph-powered machine learning to recommendation engines, fraud detection systems, natural language processing. By making connections explicit, graphs harness the power of context to help you build more accurate, real-time machine learning models.

In this interview with the book’s author, you’ll learn more about:

  • The role of graph technology in machine learning applications.
  • How graphs provide better context to improve your ML understanding and workflow.
  • How graph data science enhances four of the most common recommendation techniques: content-based, collaborative filtering, session-based, and context-aware recommendations.
  • Data modeling considerations for graph-based recommendation engines.
  • How to approach designing a hybrid recommendation engine that incorporates multiple approaches.
Dr. Alessandro Negro
Chief Scientist, GraphAware

Dr. Alessandro Negro is the Chief Scientist at GraphAware. He has been a long-time member of the graph community and he is the main author of the first-ever recommendation engine based on Neo4j. At GraphAware, he specialises in natural language processing, recommendation engines and graph-aided search. Before joining the team, Alessandro has gained over 10 years of experience in software development and has presented at many prominent conferences, such as JavaOne. Alessandro holds a Ph.D. in Computer Science from University of Salento. He is based in Southern Italy (lucky him!), but travels to clients around the world.

Amy Hodler
Director of Graph Analytics and AI Programs, Neo4j

Amy is a network science devotee, AI and Graph Analytics Program Director at Neo4j, and a co-author of the O'Reilly book, "Graph Algorithms: Practical Examples in Apache Spark and Neo4j." She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior.

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