Using ML for personalizing food search at Gojek
Who is this presentation for?
- Data scientists, data engineers, and software developers
GoFood, Gojek’s food delivery product, is one of the largest of its kind in the world. Jewel James and Mudit Maheshwari discuss the approaches considered and lessons learned during the design and successful experimentation of a search system that uses ML to personalize the restaurant results based on the user’s food and taste preferences.
The estimation of the relevance was formulated as a learning to rank ML problem, which makes the task of performing the ML inference for a very large number of customer-merchant pairs the next hurdle. Jewel and Mudit outline their findings for creating a learning model for food recommendations, targeting experiments to a certain percentage of users, training the model from real time data, and enriching restaurant data with custom tags.
Join in for tips on making design decisions on the data pipelines and software architecture needed when using ML for relevance ranking in high-throughput search systems.
- A basic understanding of the application of ML models (useful but not required)
What you'll learn
- Learn how to apply machine learning models in high throughput systems, how to build batch pipelines that enable enriching data with custom analytics, and how to design and build high throughput systems
Jewel James is a data scientist at Gojek.
Mudit Maheshwari is a product engineer at Gojek working with the GoFood search team focused on providing relevant results to the user. Previously, he’s worked on developing and designing scalable, reliable, and fault-tolerant systems for one of the biggest food delivery business.
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