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July 02

13:00 UTC   Start Times Around the World


Build Recommendation Systems Using a Graph Database
Recommendation systems are useful for retail and other user-facing applications. A graph database provides key insights that can greatly improve results. In this session, we will show how to build a recommendation system based on historical purchase data. We will show how to extract a graph from transactional tables, how to run graph algorithms and graph queries to generate recommendations, and how to visualize the graph to understand the logic.

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Session Resources

Your Experts

Ryota Yamanaka
Ryota Yamanaka, Regional Product Manager, Spatial and Graph    
Ryota Yamanaka is a regional product manager (Asia-Pacific) in spatial and graph technologies at Oracle. He has been a regular user of graph databases since he received his degrees in bioinformatics, and he is also experienced in using graphs, semantic technologies, and big data, in various industries during his 8 year career at Oracle. He has been based in Bangkok since 2018.
Melli Annamalai
Melli Annamalai, Senior Principal Product Manager    
I am a product manager at Oracle, in the database organization. Over the years I have developed extensive experience in unstructured and semi-structured data management. My current focus areas are Graphs, Apache Kafka, and Big Data. I enjoy working with customers and helping them use our technology to solve their problems, from small scale problems to those that have tremendous impact in their organization and beyond. Along with my colleagues I am on a mission to make our products as easy to use as possible. I believe in equitable access to education for all, and I spend most of my spare time volunteering in activities towards that goal.
Caroline Chan
Caroline Chan, Developer
Caroline is the developer at Cagla Inc (Japan). We are designing and developing next-generation systems for TOYOTA using such a kind of technology. She also uses x2 Oracle information to create a visualizable view for non-technical users.