Do you like to explore new dishes every time you order food? Or do you stick to your usual favourites? Do you like a Raita with your Biryani, or just a soft drink? Do you prefer veg or non veg? Do these preferences change depending on time of day or day of the week?

These are among the several questions that need to be automatically inferred from data while building a food recommendation engine at scale. Right from ranking dishes on the menu to suggesting complimentary dishes on the cart, Data Science is at the very core of constantly improving our suggestions. In this talk, we will dive into multiple scenarios that pop up while building a food recommendation system in terms of handling data sparsity and handling new restaurants / users among other challenges that crop up due to scale. We will look at different algorithms that need to be explored in order to effectively handle these various challenges.


Outline/Structure of the Talk

Proposed outline:

1. Introduction & Motivation - 7 mins

1.1 Data driven analysis to motivate why it is important to recommend and personalise

1.2 Different scenarios and challenges for food recommendation

1.3 Mathematical formulation of metrics - NDCG, MRR

2. Formulations and algorithms for different scenarios - 10 mins

2.1 Popularity & User Segments

2.2 Learning to Rank Formulation

2.3 Multi arm bandit Formulation

2.4 Multi stakeholder recommender system Formulation

3. Questions - 3 mins

Learning Outcome

* Learning to think about different scenarios, challenges and approaches while building a recommendation engine

* Get a brief introduction towards different learning algorithms like L2R, MAB

Target Audience

Anyone with intermediate understanding of Machine Learning

schedule Submitted 11 months ago

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