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Using ML for Personalizing Food Search at Go-jek
GoFood, the food delivery product of Gojek is one of the largest of its kind in the world. This talk summarizes the approaches considered and lessons learnt 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 .
We formulated the estimation of the relevance 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.
The talk will cover our learnings and findings for the following:
a. Creating a Learning Model for Food Recommendations
b. Targetting experiments to a certain percentage of users
c. Training the model from real time data
d. Enriching Restaurant data with custom tags
Our story should help the audience in making design decisions on the data pipelines and software architecture needed when using ML for relevance ranking in high throughput search systems.
- Brief about Speaker and GoJek/GoFood
- Architecture considerations
- Modelling search as a relevance problem
- Creating Machine Learning Model for Personalized Search
- Aggregating real time customer interaction data
- Tracking Performance of the model
- Training current model with real time data points
- Enriching Restaurant Data with custom metrics
- Road Ahead for improving search experience
No pre-requisite is required for the presentation.
Having knowledge about Elasticsearch and ML will help them grasp our use case better.
Maulik Soneji is currently working as a Data Engineer at Gojek where he works with different parts of data pipelines for a hyper-growth startup. Outside of learning about data systems, he is interested in elasticsearch, golang and kubernetes.