In this article, we explicate the concept of `collaborative filtering and the role it can have in making advertising more efficient, and hence, more profitable for the advertisers. It is a methodology that is growing both in its usage and in its sophistication. It may soon be part of virtually all database-marketing promotional activities.

If a consumer needs to choose among several options, and he/she is not certain what to do, he/she will often rely on word-of-mouth opinions. However, not all opinions may be equally desirable. If the consumer is, for example, a 60-year-old male, Ganesh, and he is not certain whether he would enjoy a particular book, he likely would not give much weight to the opinion of his 12-year-old granddaughter. Similarly, Ganesh may not give much weight to his daughter-in-law's opinion either she being 25 years younger than Ganesh. Still, he might give more weight to his daughter-in-law's opinion than to his granddaughter's opinion.

Now consider a situation in which Ganesh has Web access to the opinion of thousands, perhaps, millions of people concerning the book of (potential) interest to him. That is, Ganesh, perhaps due to membership in a Web-based book group, or by making a purchase, or simply by considering the making of a purchase, is able to make use of these opinions, at least in some summary forms. One option might be to present Ganesh with the average opinion of all the people. Simply to be concrete, let's say this average comes from a 1-5 scale, where 5 means "must read" and 1 means "awful;" (this is the scale used by the website, MovieLens, except, of course, that "must read" is "must see"). However, the group of all people likely includes a few people like his granddaughter, and many people like his daughter-in-law. Wouldn't it be useful to have some type of `intelligent agent' that could provide Ganesh with the opinions of only those people who have rated books Ganesh has previously read and, of these people, only those whose ratings closely matched Ganesh's own opinion of these books. Of course! Can a system such as this actually be implemented? Yes! It already exists in many settings. It is most often referred to as collaborative filtering.

Advertising Express Magazine, Collaborative Filtering, Database-Marketing Promotional Activities, Collaborative Filtering Programs, Social Filtering, Customer Relationship Management, E-commerce Websites, Word-of-mouth Marketing, Decision Making Process, Advertising Effectiveness, Customization.