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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 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 is a Data Engineer at Elsevier, working with data scientists to develop smart recommender systems.