Collaborative filtering system:

• It uses community data from peer group for recommendation.

• These exhibits all those things that are popular among the peers.

• These filtering systems recommended items based on similarity measure between users and / or items

• Here user profile and contextual parameters along with the community data are used by the recommend-er systems to personalize the recommendation list.

• This is the most prominent approach in e-commerce site.

Example:

• Consider a movie rating system

• The basic assumption for collaborative filtering is:

• User gives ratings to items in the catalog

• Customers who had similar taste in the past will have similar taste in future

• Users who agreed in their subjective evaluations in the past will agree in the future too.

• To find out similarity we can use Pearson's correlation co-efficient as:

$sim (a,b) = \sum_pep \frac{(ra, p - ra) (rb, p - rb)}{\sqrt{\sum_pep (ra, p - ra)^2, (rb 3 p - rb)^2}}$

Where

A, b = users

Ra, p – rating of user ‘a’ for item ‘p’

P – set of items rated by both a and b

• We can use ans for prediction as :

$pred (a,p) = sa + \frac {\sum_ben sim (a,b) \times (\eta , p - r b)}{\sum_ben sum (a, b)}$

• We can apply this to movie rating system and based on that we can predict rating for movie as well as to whom the movie should be recommended.

Collaborative vs content based

• Content based systems examine the properties of the item examined or recommended.

• These systems takes input from the user profile and the contextual parameters along with product features to make the recommendation list.

• Similarity of items is determined by measuring the similarity in their properties.

• These systems need some information related to the content of available items, but no complete information.

• It also requires user profiling describing what user likes.

• In movie recommendation system- content based filtering systems will recommend the movie based on users profile information like age, gender, etc as well as properties of movie like genre, actors, etc.