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SkillsCast

Adaptive Recommender Systems with Apache Spark

13th December 2018 in London at Business Design Centre

There are 50 other SkillsCasts available from Scala eXchange London 2018

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Collaborative filtering (CF) is a powerful algorithm at the core of many recommender systems. However, it is inherently naïve of features that can further improve the quality of recommendations.

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.

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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.

SkillsCast

Please log in to watch this conference skillscast.

Https s3.amazonaws.com prod.tracker2 resource 41088130 skillsmatter conference skillscast o9nohu

Collaborative filtering (CF) is a powerful algorithm at the core of many recommender systems. However, it is inherently naïve of features that can further improve the quality of recommendations.

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:

Thanks to our sponsors

About the Speakers

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.

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