Collaborative FilteringPresented By:Sadaf Baloch

MS(SE)

IntroductionCollaborative filtering is a method of :

making automatic predictions /recommendations about the interests of a user by collecting taste information from many users

The main idea is:To automate the process of "word-of-mouth" by

which people recommend products or services to one another.

Why Collaborative filtering?If you need to choose between a variety of

options with which you do not have any experience, you will often rely on the opinions of others who do have such experience.

Instead of asking opinions to each individual, you might try to determine an average opinion for the group.

However, ignores your particular interests, which may be different from those of the average person.

Who those “many users” are?Who share the same rating patterns with the

active user.

These ratings can be used to calculate prediction for the active user.

MechanismA large group of people's preferences are

taken in accountA subgroup of people is selected whose

preferences are similar to the preferences of the person who seeks advice

Average of the preferences for that subgroup is calculated

The resulting preference function is used to recommend options on which the advice-seeker has expressed no personal opinion as yet.

BottleneckCollection of Preferences

To be reliable, the system needs a very large number of people to express their preferences about a relatively large number of options .

This requires quite a lot of effort from a lot of people. Since the system only becomes useful after a "critical mass" of opinions has been collected.

People will not be very motivated to express detailed preferences in the beginning stages

The End ..

Referenceshttp://pespmc1.vub.ac.be/collfilt.html