Recommentaion engine

Content-based and collaborative filtering are two primary approaches used in recommendation systems. Here are the key differences between them:

Content-Based Recommendation Engine

Principle:

Content-based filtering recommends items similar to those that a user liked in the past based on the item's features.

How It Works:

Item Representation:

Items are represented using their features. For example, a movie could be represented by its genre, director, cast, etc.

User Profile:

A profile is created for each user based on the features of items the user has interacted with (e.g., liked, purchased, rated).

Similarity Measure:

The system calculates the similarity between items based on their features and recommends items similar to those the user has liked in the past.

Advantages:

No Cold Start for Items: New items can be recommended as long as their features are known.

Personalized Recommendations:

Tailored to individual user preferences based on item features.

Disadvantages:

Feature Engineering:

Requires detailed and high-quality features of items, which can be complex and resource-intensive.

Limited Discovery:

Users may not be exposed to items outside their existing preferences, leading to a narrow recommendation range (filter bubble).

Example Jupyter notebook

Collaborative Filtering Recommendation Engine

Principle: Collaborative filtering recommends items based on the interactions and preferences of similar users.

How It Works:

User-Item Interactions: Utilizes user-item interaction data, such as ratings, purchases, or clicks.

Similarity Measure: Two main types:

User-Based Collaborative Filtering:

Finds users similar to the target user and recommends items those similar users liked.

Item-Based Collaborative Filtering:

Finds items similar to those the target user has interacted with and recommends those items.

Matrix Factorization:

Techniques like Singular Value Decomposition (SVD) are used to factorize the user-item interaction matrix into lower dimensions for recommendations.

Advantages:

No Need for Item Features:

Can work with only interaction data.

Serendipity:

Can introduce users to items they might not have considered based on other users' preferences.

Disadvantages:

Cold Start for Users and Items:

New users or items with no interaction history are hard to recommend accurately.

Sparsity:

In systems with many items and users, the user-item interaction matrix can be very sparse, making it difficult to find similarities.

[Example Jupyter notebook] (https://github.com/BhadraNivedita/Movie-Recommendation-Engine-using-scikit-learn/blob/main/Collaborative%20recommendation%20engine.ipynb)