Adaptive Recommender Systems with Apache Spark
Please log in to watch this conference skillscast.
At Elsevier, Adam and Anna have augmented their CF-based research article recommender system with a ‘Learning to Rank’ (LTR) machine learning model that uses a rich array of article features to modify and re-rank recommendations. In addition, this model is constantly adapting to real user feedback, so that recommendation quality improves over time with no manual intervention.
In this talk, we will explore the implementation of the CF algorithm and adaptive LTR model in Apache Spark to produce demonstrably higher quality recommendations to our users, and look at how Spark allows developers and data scientists to work together on a web-scale production system.
YOU MAY ALSO LIKE:
- Embrace the Implicit (SkillsCast recorded in December 2018)
- ScalaCon 2021: November Edition (Online Conference on 2nd - 5th November 2021)
- Adopting F# on a Consultancy Project: From Zero to MVP to V0 Launch (SkillsCast recorded in October 2021)
- Deep Learning with F#: An Experience Report (SkillsCast recorded in October 2021)
Thank you to our sponsors and partners
Platinum
Gold
Silver
Bronze
Partners
Adaptive Recommender Systems with Apache Spark
Anna Bladzich
Anna is a Senior Data Engineer at Elsevier. She has been a Scala developer for 4 years, working for start-ups before joining the world of research. Anna works on various recommendation systems utilising the latest research in data science and machine learning. She loves all things functional, cats and knitting.
Adam Davidson
Adam is a Data Engineer at Elsevier, working with data scientists to develop smart recommender systems.