Recommender Systems

1. Content-Based Filtering
2. Collaborative Filtering
3. Hybrid Systems

Recommender systems are one of the most practical applications of machine learning and data science. They are used to suggest products, services, or content to users based on their interests, behavior, or similarities with other users. These systems are present in most modern platforms, from movie suggestions on Netflix to products on Amazon or posts on social media.

What is a recommender system?

A recommender system is a software tool that helps users discover items of interest. Some examples of items a system can recommend are:

Movies or series (Netflix, Hulu)
Products (Amazon, Mercado Libre)
Songs or playlists (Spotify, Apple Music)
News articles (Google News, Pocket)
People or profiles (LinkedIn, Facebook)

The main goal is to filter and prioritize relevant information for the user, improving their experience and reducing information overload.

Main types of recommender systems

1. Content-Based Filtering

This type of system recommends items similar to those the user has consumed or rated positively in the past. It relies on the features of the content (e.g., genre, keywords, author).

How it works:

Each item is represented as a feature vector.
A user profile is built based on the items they liked.
Similarities (e.g., cosine similarity) are calculated between the user profile and new items.
The most similar items are recommended.

Example: If a user has watched action movies with fast-paced plots and strong protagonists, the system will recommend other movies with those characteristics, even if no one else has watched them.

Advantages	Disadvantages
Individual personalization	Risk of over-specialization
Does not require data from other users	Does not discover new interests
Useful for recommending less popular items	Limited to what the user has already seen

2. Collaborative Filtering

This approach is based on the interactions of multiple users. It assumes that “if a group of similar users likes an item, you are likely to like it too.”

Two main approaches:

User-based: Finds users similar to the target user and recommends items they liked.
Item-based: Finds items similar to those the user has rated positively and recommends them.

Common algorithms:

k-Nearest Neighbors (k-NN)
Pearson Correlation
Matrix Factorization techniques like SVD

Example: If two users have similar tastes and one of them rates a new movie positively, that movie will be recommended to the other user.

Advantages	Disadvantages
Discovers unexpected content	Cold start problem: new users or items with insufficient data
Leverages collective trends	Sparse data matrix: few items rated per user

3. Hybrid Systems

These combine multiple approaches to achieve better results. Typically, they mix content-based and collaborative filtering.

Common hybridization strategies:

Weighted hybrid: Combines results from different models with varying weights.
Switched hybrid: Uses one model or another depending on the situation.
Cascade hybrid: One model filters candidates, and another ranks them.
Meta-level hybrid: One model feeds into another (e.g., using content profiles within a collaborative model).

Advantages	Disadvantages
Higher accuracy	Increased implementation complexity
Solves cold start and over-specialization problems	Requires coordination between models
More robust to changes in data

Algorithmic Foundations

To build these systems, knowledge of some mathematical and computational tools is necessary:

Similarity Measures

Cosine similarity: Measures the angle between two vectors.
Pearson correlation: Measures the linear relationship between ratings.
Jaccard index: Measures the overlap between binary sets.

Dimensionality Reduction

SVD (Singular Value Decomposition)
NMF (Non-negative Matrix Factorization)
Helps uncover latent factors that explain preferences.

Feature Representation

TF-IDF: Text representation based on frequency and importance.
One-hot encoding: For categorical variables.
Embeddings: Dense representations useful in advanced models and neural networks.

Recommender systems are fundamental in modern applications. There are different approaches, each with advantages and disadvantages. Designing an appropriate system will depend on the type of data available, the system’s goal, and computational resources.