To build a recommendation system to recommend products to customers based on the their previous ratings for other products
https://drive.google.com/file/d/1ClBptsK3V5KgKXtK2GSRzFNAW7GnTPDW/view?usp=sharing
For this model, we are using the Electronics dataset from Amazon Reviews data repository.
E- Commerce
Online E-commerce websites like Amazon, Flipkart uses different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time.
userId: Every user identified with a unique id
productId: Every product identified with a unique id
Rating: Rating of the corresponding product by the corresponding user
timestamp: Time of the rating ( ignoring this column for our experiment)
●Exploratory Data Analysis
●Creating a Recommendation system using real data
●Collaborative filtering
1.Read and explore the given dataset.
2.Take a subset of the dataset to make it less sparse/ denser.
3.Split the data randomly into train and test dataset.
4.Build Popularity Recommender model.
5.Build Collaborative Filtering model.
6.Evaluate both the models.
7.Get top -K ( K = 5) recommendations. Since our goal is to recommend new products foreach user based on his/her habits, we will recommend 5 new products.
8.Summarise your insights.