Using ML for Personalizing Food Search
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 Search
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.
Outline/Structure of the Talk
- Brief about Speaker and GoJek/GoFood
- Current Search Architecture at GoFood
- Challenges of latency vs search relevancy with personalization
- Butler Architecture
- Creating Machine Learning Model for Personalized Search
- Tracking Performance of the model
- Training current model with real time data points
- Enriching Restaurant Data with custom metrics
- Running multiple experiments for targeted users
- Road Ahead for improving search experience
Attendees will learn about how to apply Machine Learning for search relevancy systems.
They will understand how to make tradeoffs between search relevance and response times in high throughput systems.
Attendees will also understand about how to use ML Model along with elasticsearch for re-ranking documents.
Data Scientists, Product Analysts
Prerequisites for Attendees
No pre-requisite is required for the presentation.
Having knowledge about Elasticsearch and ML will help them grasp our use case better.
schedule Submitted 2 months ago
People who liked this proposal, also liked:
Dipanjan Sarkar - Explainable Artificial Intelligence - Demystifying the HypeDipanjan SarkarData ScientistRed Hat
schedule 2 months agoSold Out!
The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.
A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based). The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.
To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!