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Real-time Recommendations for Retail: Architecture, Algorithms, and Design

Jonathan Natkins (WibiData), Juliet Hougland (Cloudera)
Data Science Beekman Parlor - Sutton North
Average rating: ****.
(4.20, 10 ratings)
Slides:   1-PPTX 

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 Outdated recommendations decrease the likelihood of presenting the right offer and make it harder to maintain customer loyalty.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:

  • Architecture of the recommendation serving platform
  • Choice of recommendation algorithm
  • Datastore access patterns

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.

Photo of Jonathan Natkins

Jonathan Natkins


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.

Photo of Juliet Hougland

Juliet Hougland


Juliet is a Senior Data Scientist at Cloudera, and contributor/committer/maintainer for the Sparkling Pandas project. Her commercial applications of data science include developing predictive maintenance models for oil & gas pipelines at Deep Signal, and designing/building a platform for real-time model application, data storage, and model building at WibiData. Juliet was the technical editor for Learning Spark by Karau et al. and Advanced Analytics with Spark by Ryza et al. She holds an MS in Applied Mathematics from University of Colorado, Boulder and graduated Phi Beta Kappa from Reed College with a BA in Math-Physics.

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Picture of Juliet Hougland
Juliet Hougland
10/29/2013 4:18pm EDT

I just uploaded our slides to slideshare: Hope you find it useful!

Picture of Benjamin Bengfort
Benjamin Bengfort
10/29/2013 3:35pm EDT

Will the slides be made available?


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