> For the complete documentation index, see [llms.txt](https://dailyjournal.gitbook.io/ds-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dailyjournal.gitbook.io/ds-ai/machine-learning/applications/recommendation-system.md).

# Recommendation System

## Content-Based Filtering

#### Approaches

* Classification
* Regression

## Collaborative Filtering

Approaches

* Model-based
  * Matrix Factorisation
* Memory-based
  * user-user
  * item-item

## Problems

### Cold Start Problem

* Representative based 
  * use a subset of items and users that represent the population.
* Content-based
  * use side information such as text, social networks, etc.
* Bandit
  * consider the exploration vs exploitation tradeoff in new items.
* Deep Learning
  * recent methods that try to solve some of the issues tackled above but using a black box.

#### Solution - Content + Collaborative = Hybrid Filtering

### Grey Sheep Problem

#### Solution - Content + Collaborative = Hybrid Filtering

## Evaluation

### Metrics-based Evaluation

### Human-based Evaluation