Jerry Vos is a data scientist at Wehkamp, the Netherlands. He works on developing machine-learning applications based on the large-scale dataset in e-commerce. He has worked on a wide range of projects within the company including customer segmentation, customer lifetime value and several projects to improve the customers’ recommendation experience. He has a background in Econometrics (Rijks Universiteit Groningen).
October 16, 2019 05:00 PM PT
As a leading e-commerce company in fashion in the Netherlands, Wehkamp dedicates itself to provide a better shopping experience for the customers. Using Spark, the data science team is able to develop various machine-learning projects for this purpose based on the large scale data of products and customers. A major topic for the data science team is ranking products. If a visitor enters a search phrase, what are the best products that fit the search phrase and in what order should the products been shown? Ranking products is also important if a visitor enters a product overview page, where hundreds or even thousands of products of a certain article type are displayed.
In this project, Spark is used in the whole pipeline: retrieving and processing the search phrases and their results, making click models, creating feature sets, training and evaluating ranking models, pushing the models to production using ElasticSearch and creating Tableau dashboarding. In this talk, we are going to demonstrate how we use Spark to build up the whole pipeline of ranking products and the challenges we faced along the way.