# Flipkart Grid

### Fixed Effect
- Demographics
- Age group
- Staple Diet
- Brand Frequency
- **Recipe** (Identify Items in Foods in proportion to estimate monthly requirements)
- Spending Power
- Occasions

Festive Foods products will be tracked during festivals at a specific demographics. Offers will be provided.

### Random Effect
- Search
- Click
- Comment
- Rating
- Order History
- Health Data (Get health data from fitness app will help us recomend health based products. e.g. using weight data for high weight people we can recommend low protein foods. Using heart data we can recommend cholestrol free foods.)

## Technology

- GDMix - Deep ranking personalisation framework (https://github.com/linkedin/gdmix)
- Detext (https://github.com/linkedin/detext)
- Tracked Queue - Auto handles queue based on capacity.
- Ionic Framework
- Elastic Search

## Personalisation
- Collaborative Filtering (Reinforcement Learning + RNN)

## Blogs
- https://towardsdatascience.com/building-a-food-recommendation-system-90788f78691a
- https://en.wikipedia.org/wiki/Fixed_effects_model
- https://en.wikipedia.org/wiki/Random_effects_model
- https://engineering.linkedin.com/blog/2020/gdmix--a-deep-ranking-personalization-framework

### Time of Visit

### Relevant products purchased by similar users

K-Nearest Neighbour Algorithm
Overall Purchased Analysis
K-Means Clustering
Association Rule Mining