8. Recommendation Systems

Recommendation systems suggest relevant content to users based on historical interactions, user behavior, or content similarity. These systems drive user engagement across streaming platforms, e-commerce sites, and social media.

8.1 Collaborative Filtering

Collaborative Filtering relies on historical user-item interactions. It assumes that users with similar preferences in the past will continue to share tastes.

8.1.1 Matrix Factorization

This technique decomposes the large sparse user-item rating matrix into two lower-dimensional matrices: user features and item features.

predicted_rating = user_vector · item_vector

Key Points:

Captures latent factors like genre, price, or brand preferences
Reduces dimensionality of interaction data
Used in systems like Netflix and Amazon

8.1.2 Gradient Descent

Used to minimize the prediction error in matrix factorization using a loss function with regularization to avoid overfitting.

L = Σ(R_ui - U_u · V_i)^2 + λ (||U_u||² + ||V_i||²)

R_ui: Real user rating
U_u and V_i: Latent vectors
λ: Regularization parameter

Key Points:

Optimizes user/item vectors to reduce prediction error
Gradient Descent is iterative and scalable
Regularization prevents overfitting

8.1.3 Applications

Movie streaming platforms recommending based on similar users' ratings
E-commerce suggesting items bought together
Social networks recommending friends or connections

Key Points:

Most effective when user interaction history is rich
Requires large datasets to perform well
Struggles with new users or new items (cold start)

8.2 Content-Based Filtering

Instead of relying on user interactions, this method recommends items with similar characteristics to those the user already liked.

8.2.1 Feature Vectors

Items are represented using structured features like genre, price, brand, or keywords.

[Genre=Action, Rating=4.5, Runtime=120]

User profile is derived by averaging features of previously liked items.

Key Points:

Uses explicit item features to drive recommendations
Good for users with limited interaction history
Does not require information from other users

8.2.2 Neural Networks

Advanced models like deep neural networks can learn complex user-item relationships using embeddings and dense layers.

Use input features like reviews, categories, and images
Train to predict relevance scores

Key Points:

Powerful for high-dimensional and unstructured data
Requires large labeled datasets
Supports personalization at scale

8.2.3 Similarity

Measures like cosine similarity and Euclidean distance determine how alike two items are.

cosine_similarity(A, B) = (A · B) / (||A|| * ||B||)

Key Points:

Core technique in content-based filtering
Simple and interpretable
Fast computation for small/medium-sized datasets

8.3 Hybrid Approach

The hybrid method merges collaborative and content-based filtering to overcome limitations of each individual approach.

Combine scores or rankings from both systems
Use meta-learners or weighted models

Key Points:

Balances cold start and personalization
More accurate and robust than standalone methods
Widely used in production systems like Netflix and Spotify

Technique	Description	Strengths
Collaborative Filtering	Uses user-item interactions to recommend	Highly personalized, scalable
Content-Based Filtering	Recommends based on item features	Works well for new users or unique interests
Hybrid Approach	Combines collaborative and content methods	Improved accuracy, solves cold-start issues