Collaborative filtering is the technique of making automatic predictions about the interests of the user by gathering taste and preference information from many users. In this sense, it is collaborative by collecting, or collaborating with all other users. The underlying assumption of the collaborative filtering approach is that those who agreed in the past are inclined to agree again in the future. Take for instance, a collaborative filtering or recommendation system for music tastes could make predictions about which music a user should like given a partial list of that user's tastes. These predictions however, are specific to the user, but use information gleaned from many users. This differs from the more simple approach of giving an average, non-specific score for each item of interest, for example based on its number of votes.
Collaborative filtering systems usually take two steps. The first of these is to look for users who share the same rating patterns with the active user. This means the user whom the prediction is for. Secondly, collaborative filtering uses the ratings from those like-minded users found in the first step to calculate a prediction for the active user. Alternatively, item-based collaborative filtering popularized by Amazon.com (users who bought x also bought y) and first proposed in the context of rating-based collaborative filtering by Vucetic and Obradovic in 2000, proceeds in an item-centric manner, first building an item-item matrix determining relationships between pairs of items. Then, using the matrix, and the data on the current user, it infers his taste.
Another form of collaborative filtering can be based on implicit observations of normal user behavior (as opposed to the artificial behavior imposed by a rating task). In these systems you observe what a user has done together with what all users have done (what music they have listened to, what items they have bought) and use that data to predict the users behavior in the future or to predict how a user might like to behave if only they were given a chance. These predictions then have to be filtered through business logic to determine how these predictions might affect what a business system ought to do. It is, for instance, not useful to offer to sell somebody some music if they already have demonstrated that they own that music.
In the age of information explosion such techniques can prove very useful as the number of items in only one category (such as music, movies, books, news, web pages) have become so large that a single person cannot possibly view them all in order to select relevant ones. Relying on a scoring or rating system which is averaged across all users ignores specific demands of a user, and is particularly poor in tasks where there is large variation in interest, for example in the recommendation of music. Obviously, other methods to combat information explosion exist such as web search, data clustering, and more.
The three types of filtering include passive, active, and item based filtering.
Web personalization, on the other hand, is based on the interests of an individual. Personalization implies that the changes are based on implicit data, such as items purchased or pages viewed. The term customization is used instead when the site only uses explicit data such as ratings or preferences.
On an intranet or B2E Enterprise Web portals, personalization is often based on user attributes such as department, functional area, or role. The term customization in this context refers to the ability of users to modify the page layout or specify what content should be displayed.
There are two categories of personalization are both rule-based and content-based. An example of personalization would include MyYahoo, where the user is allowed to design his or her page according to their personal desires. This is different from collaborative filtering in the sense that the user is the only one influencing the information. The information presented is only that which the user chooses, rather than assuming like-minded users would possess similar interests, therefore similar data.

Works Cited

"Collaborative Filtering." Wikipedia: The Free Encyclopedia. 6 February 2007. 11 February 2007 ://en.wikipedia./wiki/Collaborative_filtering

“Personalization.” Wikipedia: The Free Encyclopedia. 17 January 2007. 11 February 2007 <>.

Shardanand, Upendra. Social Information Filtering: Algorithms for Automating "Word of Mouth.” Penn State and NEC. 1995. 11 February 2007.