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.
