Ishita Mathur
Data Scientist
GO-JEK Tech
location_on India
Member since 11 months
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Ishita Mathur
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Ishita has worked as a Data Scientist for 3 years with product-based startups in understanding business concerns in various domains and formulating them as technical problems that can be solved using data and ML. Her current work at GO-JEK involves end-to-end development of ML projects, by working as part of a product team in defining, prototyping and implementing data science models within the product. She has also published a book on "Applied Supervised Learning with Python" with publisher Packt.
Ishita has completed her Masters’ degree in High Performance Computing with Data Science from the University of Edinburgh, UK and her Bachelors’ degree with Honours in Physics from St. Stephen’s College, Delhi.
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How GO-FOOD built a Query Semantics Engine to help you find food faster
45 Mins
Case Study
Beginner
Context: The Search problem
GOJEK is a SuperApp: 19+ apps within an umbrella app. One of these is GO-FOOD, the first food delivery service in Indonesia and the largest food delivery service in Southeast Asia. There are over 300 thousand restaurants on the platform with a total of over 16 million dishes between them.
Over two-thirds of those who order food online using GO-FOOD do so by utilising text search. Search engines are so essential to our everyday digital experience that we don’t think twice when using them anymore. Search engines involve two primary tasks: retrieval of documents and ranking them in order of relevance. While improving that ranking is an extremely important part of improving the search experience, actually understanding that query helps give the searcher exactly what they’re looking for. This talk will show you what we are doing to make it easy for users to find what they want.
GO-FOOD uses the ElasticSearch stack with restaurant and dish indexes to search for what the user types. However, this results in only exact text matches and at most, fuzzy matches. We wanted to create a holistic search experience that not only personalised search results, but also retrieved restaurants and dishes that were more relevant to what the user was looking for. This is being done by not only taking advantage of ElasticSearch features, but also developing a Query semantics engine.
Query Understanding: What & Why
This is where Query Understanding comes into the picture: it’s about using NLP to correctly identify the search intent behind the query and return more relevant search results, it’s about the interpretation process even before the results are even retrieved and ranked. The semantic neighbours of the query itself become the focus of the search process: after all, if I don’t understand what you’re trying to ask for, how will I give you what you want?
In the duration of this talk, you will learn about how we are taking advantage of word embeddings to build a Query Understanding Engine that is holistically designed to make the customer’s experience as smooth as possible. I will go over the techniques we used to build each component of the engine, the data and algorithmic challenges we faced and how we solved each problem we came across.
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