Real-time Recommender Systems Made Easy with Neo4j - Beginners
Please log in to watch this conference skillscast.
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.
YOU MAY ALSO LIKE:
- Redis as a Multi Model Database for Faster Search, ML Model Serving, Graph Traversal and Much More! (SkillsCast recorded in July 2018)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- ProgNET London 2019 (in London on 11th - 13th September 2019)
- Haskell eXchange 2019 (in London on 10th - 11th October 2019)
- Keynote by Rob Sewell on Administrating SQL Server with PowerShell dbatools (in London on 19th August 2019)
- Telling Human Stories With Data (in London on 19th August 2019)
- How real-time data processing is used for application in customer experience? (SkillsCast recorded in July 2019)
- A Look at the technology of LUNO and Adjust (SkillsCast recorded in July 2019)
Thanks to our sponsors
Real-time Recommender Systems Made Easy with Neo4j - Beginners
Pieter Cailliau
Pieter is part of the Solutions Architect team of RedisLabs 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 RedisLabs, Pieter used to work for Neo4j and was an instanceof Software Engineer at TomTom, the world’s leader in location and navigation software.
Please log in to watch this conference skillscast.
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.
YOU MAY ALSO LIKE:
- Redis as a Multi Model Database for Faster Search, ML Model Serving, Graph Traversal and Much More! (SkillsCast recorded in July 2018)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- ProgNET London 2019 (in London on 11th - 13th September 2019)
- Haskell eXchange 2019 (in London on 10th - 11th October 2019)
- Keynote by Rob Sewell on Administrating SQL Server with PowerShell dbatools (in London on 19th August 2019)
- Telling Human Stories With Data (in London on 19th August 2019)
- How real-time data processing is used for application in customer experience? (SkillsCast recorded in July 2019)
- A Look at the technology of LUNO and Adjust (SkillsCast recorded in July 2019)
Thanks to our sponsors
Real-time Recommender Systems Made Easy with Neo4j - Beginners
Pieter Cailliau
Pieter is part of the Solutions Architect team of RedisLabs 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 RedisLabs, Pieter used to work for Neo4j and was an instanceof Software Engineer at TomTom, the world’s leader in location and navigation software.