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by Patrick Titzler, Margriet Groenendijk | Published September 24, 2018
Artificial intelligenceData ScienceDeep LearningMachine Learning
Most websites selling products online show you a list of items that you might be interested in. The better the recommendations the more likely that you will buy any of these, which will increase their sales. But how are these recommendations created? This code pattern shows you how to build a recommendation engine from customer data with Jupyter Notebooks, Apache Spark, and PixieDust, which are all open source projects. When combined with Watson Studio and Watson Machine Learning you can quickly produce an interactive dashboard to explore and test a recommendation model.
Using purchase data from all customers is the fastest way to create recommendations. With this data, you’re able to create groups (clusters) of customers that have bought similar products. Within each cluster are customers who are more similar to each other than the customers in other groups.
In this code pattern, we use historical shopping data to build a recommendation engine with Spark and Watson Machine Learning. The model is then used in an interactive PixieApp in which a shopping basket is simulated and used to create a list of recommendations.
When you have completed this code pattern, you will understand how to:
Find the detailed instructions in the README file. These steps will explain how to:
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