## [Introduction to Recommender Systems: Non-Personalized and Content-Based on Coursera](https://www.coursera.org/learn/recommender-systems-introduction/home/welcome)

Information retrieval is the practice of asking questions about large documents. 
   - It became especially popular when doing discovery for lawsuits
   - or AWS in guiding you to the relevant products
   - One of the first recommenders was GroupLens for newsnet 

**Collaborative Filtering:** Involves running Ratings and Correlations through a CF engine. 
 - The goal is to find a neighborhood of users
 -  Recommendation Interfaces: Suggestion, top n
 -  Prediction interfaces: Evaluate candidates and score/predictive rating 

Ratings: can be implicit or explicit
Preditions: Estimate of preferences around how much you like something

**Approaches to Recommendations: **


Content-Based Approaches: 

https://www.w3.org/Conferences/WWW4/Papers/93/

 - Non-personalized/Stereotype (overall preference based on population)
 - Product Association: People who bought x also want x - also not personalized
 - Content-based: Based on metadata
 - Collaborative filtering: Learning preferences and using the community

Preferences and Ratings:

Preference: We want to learn what users like (can be broad)
  Rate / Review - Explicit
  Click/purchase/follow - Implicit

Explicit