Session-based Recommendation Using SR-GNN

• 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

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• 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

Data Set Information

• 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.csv file.