Retrieving Visually-Similar Products for Shopping Recommendations using Spark and Tensorflow - Databricks

Retrieving Visually-Similar Products for Shopping Recommendations using Spark and Tensorflow

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As an e-commerce company leading in fashion and lifestyle in the Netherlands, Wehkamp dedicates itself to provide a better shopping experience for customers. Using Spark, the data science team is able to develop various machine-learning projects that improve the shopping experience.

One of the applications is to create a service for retrieving visually similar products, which can then be used to show substitutional products, to build visual recommenders and to improve the overall recommendation system. In this project, Spark is used throughout the entire pipeline: retrieving and processing the image data, training model distributedly with Tensorflow, extracting image features, and computing similarity. In this talk, we are going to demonstrate how Spark and the Databricks enable a small team to unify data and AI workflows, develop a pipeline for visual similarity and train dedicated neural network models.

 

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About Zhichao Zhong

Wehkamp

Zhichao Zhong is a data scientist at Wehkamp in the Netherlands. He is enthusiastic about applying machine-learning to create a fresh online shopping experience. He has developed several projects to improve the visual and recommendation experience for the customers. He received his Ph.D. degree from Leiden University and conducted his Ph.D. research at CWI, Amsterdam.