-
In this project we are building Session Based Recommendation system Using Graph Neural Network.
-
We have used real world E-comm data which available on Kaggle : Retail Rocket Dataset.
-
For Implementing Graph Neural Network we have utiliesd PyGeometric Package which build on top of Pytorch.
-
Author : Lokesh Baviskar
-
Email Id : lokeshbaviskar4@gmail.com
-
On gross level Project flow as below:
1. Constructing Session Graphs
2. Learning Item Embeddings on Session Graphs
3. Generating Session Embeddings
4. Making Recommendation and Model Training
- We Have Utilised Retail Rocket Dataset ( Which is real world data collected from ecommerce platform ) for building Session Based Recommender.
- You can download the data from 🔗this kaggle competition.
- The behaviour data, i.e. events like clicks, add to carts, transactions, represent interactions that were collected over a period of 4.5 months.
- A visitor can make three types of events, namely “view”, “addtocart” or “transaction”.
- In total there are 2,756,101 events including 2,664,312 “view”, 69,332 “addtocart” and 22,457 “transaction” produced by 1,407,580 unique visitors.
- The file with item properties includes 20,275,902 rows, i.e. different properties, describing 417,053 unique items.
- We will only use the
events.csvfile.
