Hybrid Recommendations: Case Study

Download the dataset below to solve this Data Science case study on Hybrid Recommendations.

In the world of fashion, understanding customer preferences and providing personalized recommendations are essential for e-commerce platforms to improve customer satisfaction and boost sales. By using a hybrid recommendation system, we can provide more accurate and personalized recommendations to users.

The dataset contains information about user-product interactions in an online fashion store. The dataset includes the following features:

  • User ID: Unique identifier for each user.
  • Product ID: Unique identifier for each product.
  • Product Name: The name or description of the product.
  • Brand: The brand or manufacturer of the product.
  • Category: The category to which the product belongs (e.g., Men’s Fashion, Women’s Fashion).
  • Price: The price of the product.
  • Rating: The user’s rating for the product (on a scale of 1 to 5).
  • Colour: The colour of the product.
  • Size: The size of the product.

Your goal is to develop a hybrid recommender system that combines collaborative filtering and content-based filtering approaches. You can use collaborative filtering to leverage past user-product interactions to identify similar users and recommend products that other similar users have liked. On the other hand, you can use content-based filtering to analyze product characteristics (such as product name, brand, and category) to find similar products in terms of attributes.

References to Solve this Data Science Case Study