Recommendation system

All unapproved Citizendium articles may contain errors of fact, bias, grammar etc. A version of an article is unapproved unless it is marked as citable with a dedicated green template at the top of the page, as in this version of the 'Biology' article. Citable articles are intended to be of reasonably high quality. The participants in the Citizendium project make no representations about the reliability of Citizendium articles or, generally, their suitability for any purpose. This article is currently being developed as part of an Eduzendium student project. The course homepage can be found at CZ:Special_Topics_2010. To provide students with experience in collaboration, you are warmly invited to join in here, or to leave comments on the discussion page. The anticipated date of course completion is 13 August 2010. One month after that date at the latest, this notice shall be removed. Besides, many other Citizendium articles welcome your collaboration!

A recommendation system is a software program which attempts to narrow down selections for users based on their expressed preferences, past behavior, or other data which can be mined about the user or other users with similar interests.

Classification

The current generation of recommendation methods can be broadly classifed into the following five categories, based on the knowledge sources they use to make recommendations.:
1. Content-based recommendations.
2. Collaborative recommendations.
3. Knowledge-based recommendations.
4. demographic recommendations.
5. Hybrid recommendations.

General requirements for recommendation systems

To make a viable recommendation, three things are needed: (i) background information - the information that the system has before the recommendation process begins. (ii) input information - the information that a user must enter to the system in order to trigger a recommendation. (iii) an algorithm that combines background and input information to arrive at its suggestions.

Content-based recommendation

In Content-based recommendation, the user receives recommendations based on his past preferences.

Collaborative RS

Collaborative recommendation systems recommend items that people with similar taste preferred in the past.

Knowledge-based

Utilizes the knowledge about users and products and reasons out what products meet the users requirements. Some of the systems being used at present effectively walk the user down a discrimination tree of product features whereas others have adopted a quantitative decision support tool for this task.

Hybrid RS

Hybrid systems use a combined content-based and collaborative approach.

References

1. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions