Product RecSys
About
Using the customer orders and rating data from Olist E-Commerce Public Dataset, which has information about 100k orders made at multiple marketplaces in Brazil from 2016 to 2018, I trained 3 models that generate product recommendations.
NOTE - The granularity of orders in the dataset is at the product category level, thus recommendations are product categories in the true sense.
Related Links
NOTE: The web app is deployed on a free server which goes into sleep mode and takes about 10s for boot up, once loaded the bot will work as expected.
Architecture
The website is divided into 3 sections.
The website is divided into 3 sections.
1. Top Trending
Demographic Filtering - Recommends highest rated products in the dataset.
Method - Uses IMDB weighted average formula and recommends highest rated products.
2. Similar Products
Item based collaborative filtering - Takes user input of one product and recommends 5 similar products.
Method - Computes cosine similarity between selected product and others based on historical ratings given by customers using KNN Basic algorithm.
3. Products you might like
User based collaborative filtering - Takes user input & rating of one product and recommends 3 products based on the rating given for the selected product.
Method - Approximates the user’s taste by running two algorithms in the backend, first computes the most similar user who has sufficient historical data, and second, makes recommendations based on their taste.