Collaborative Filtering - Introduction

Introduction

The growth of the Internet has made it much more difficult to effectively extract useful information from all the available online information. The overwhelming amount of data necessitates mechanisms for efficient information filtering. One of the techniques used for dealing with this problem is called collaborative filtering.

The motivation for collaborative filtering comes from the idea that people often get the best recommendation from someone with similar taste. Collaborative filtering explores techniques for matching people with similar interests and making recommendations on this basis.

Collaborative filtering algorithms often require (1) users’ active participation, (2) an easy way to represent users’ interests to the system, and (3) algorithms that are able to match people with similar interests.

Typically, the workflow of a collaborative filtering system is:

1. A user expresses his or her preferences by rating items (e.g. books, movies or CDs) of the system. These ratings can be viewed as an approximate representation of the user's interest in the corresponding domain.
2. The system matches this user’s ratings against other users’ and finds the people with most “similar” tastes.
3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user (presumably the absence of rating is often considered as the unfamiliarity of an item)

A key problem of collaborative filtering is how to combine and weight the preferences of user neighbors. Sometimes, users can immediately rate the recommended items. As a result, the system gains an increasingly accurate representation of user preferences over time.