Users are constantly searching for new content and to stay competitive organizations must act immediately based on up-to-date data. Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty. In order to provide the most relevant recommendations and increase engagement, organizations must track customer interactions and re-score recommendations on the fly.
Data sources have expanded dramatically to include a wealth of historical data and a constant influx of behavior data. The key to moving from predictive models, applied in batch, to models that provide responses in real time, is to focus on the efficiency of model application. The speed that recommendations can be served is influenced by:
In this presentation, we’ll discuss how developers can use open source components like HBase and Kiji to develop low-latency recommendation models that can be easily deployed by e-commerce companies. We will give practical advice on how to choose models and design data stores that make use of the architecture and quickly serve new recommendations.
Jonathan Natkins is a Member of Technical Staff on the Field Engineering team at WibiData. He helps customers use their data to create better application experiences. Prior to WibiData, Jonathan was an engineer at Cloudera, working primarily on Cloudera Manager and contributing to various Hadoop related projects. Before joining Cloudera, Jonathan worked both as an engineer and a field engineer at Vertica, first building core database features and then working closely with customers to help them move their systems into production. Jonathan holds an Sc.B in Math-Computer Science from Brown University.
Juliet Hougland answers complex business problems using statistics to tame multiterabyte datasets. She succeeds in applying and explaining the results of mathematical models across a variety of industries including software, industrial energy, retail, and consumer packaged goods. Juliet is currently the head of data science, engineering at Cloudera, where she focuses on using data to help engineering build high-quality products. Juliet’s been sought after by Cloudera’s customers as a field-facing data scientist advising on which tools to use, teaching how to use them, recommending the best approach to bring together the right data to answer the business problem at hand, and building production machine-learning models. For many years, Juliet has been a contributor in the open source community working on projects such as Apache Spark, Scalding, and Kiji. Juliet holds an MS in applied mathematics from the University of Colorado, Boulder and graduated Phi Beta Kappa from Reed College with a BA in math-physics.
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