Collaborative-Filtering-Recommender-System-for-amazon-products

products Recommender System

Dataset : https://drive.google.com/file/d/1geMKWWH0wS4-SnbM9zoXhqAfiFAlvoKl/view?usp=sharing

Collaborative Filtering

Steps:

• Get products dataframe (given data)
• Get users ratings dataframe (given data)
• Get input user ratings (user input/assumption)
• Learning the similarity weights (Pearson Correlation)
• Find the recommendations (user profile * original products categories)

Advantages and Disadvantages of Collaborative Filtering

Advantages

• Takes other user's ratings into consideration
• Doesn't need to study or extract information from the recommended item
• Adapts to the user's interests which might change over time

Disadvantages

• Approximation function can be slow
• There might be a low of amount of users to approximate
• Privacy issues when trying to learn the user's preferences

Content-Based Filtering

Steps:

• Get products dataframe with categoties (given data)
• Get input user ratings (user input/assumption)
• Weighing the categories (input user ratings * user products categories)
• Get input user profile (sum of user weighted categories)
• Find the recommendations (user profile * original products categories)

Advantages and Disadvantages of Content-Based Filtering

Advantages

• Learns user's preferences
• Highly personalized for the user

Disadvantages

• Doesn't take into account what others think of the item, so low quality item recommendations might happen
• Determining what characteristics of the item the user dislikes or likes is not always obvious