Recommendation system

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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.

History

Recommendation systems are a spinoff from a system called "Usenet," a worldwide distributed discussion system originating at Duke University in the late 1970s. Usenet operated in a client/server format, allowing user input that was categorized into specific "newsgroups." In Usenet, the posts made by users are categorized into these newsgroups, which are then further divided into sub-categories, if needed.

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.

1.Content-based recommendation

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

2.Collaborative RS

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

3.Knowledge-based recommendation

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 attributes whereas others have adopted a quantitative decision support tool for this task.

5.Hybrid RS

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

Recent Press

Wired.com recently released a great article on Caterina Fake and her work with Hunch.com especially with respect to the cold start problem.

References

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