Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item.

Two basic types of recommender systems:

- Collaborative filtering: based on collecting and analyzing a large amount of information on users’ behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself.

- Content based: based on a description of the item and a profile of the user’s preference.

- Hybrid Recommender Systems: Mix of the above too. E.g., netflix

Important factors of recommender system algorithms:

- diversity
- persistence
- privacy
- user demographics
- robustness
- serendipity
- trust
- labelling
- mobile recommender system

--

本文内容来自: 
[1] http://en.wikipedia.org/wiki/Recommender_system