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Recommender system technology is the core of Netflix and Amazon's business model and has lead to a tremendous increase in sales and customer satisfaction. Other retailers have seen sales increases of 5-15%, and now recommender systems are making their way to other industries to help customers find products faster, help salespeople find collateral and configure solutions, and help companies accelerate their product development by finding the right components to make products that meet market needs.
Real-time recommender systems are one of the sweetspot use cases for native graph databases. Key goals for a good recommender system include relevance, novelty, serendipity and recommendation differentiation. In this talk, Pieter will demonstrate how you can have full and accurate control of the recommender system with Neo4j, interactive response at scale, and "on the fly" tuning for a fast time to market.
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Real-time Recommender Systems Made Easy with Neo4j - Beginners
Pieter is part of Neo Technology’s Field Engineering team based in London. He holds a MSc in Computer Science from Ghent University, where he wrote a distinguished thesis on time-based graph models. Prior to joining Neo Technology, Pieter was an instanceof Software Engineer at TomTom, the world’s leader in location and navigation software, where he introduced neo4j to enable real-time impact analysis on their map.