Abstract:
Recommender systems use the opinions of a
community of users to help individuals in that
community more effectively identify content of
interest from a potentially overwhelming set of
choices. One of the most successful technologies for
recommender systems, called collaborative filtering,
has been developed and improved over the past
decade to the point where a wide variety of
algorithms exist for generating recommendations.
Item-based collaborative filtering algorithms have
been presented to deal with scalability problems
associated with user-based collaborative filtering.
The computation of item-based collaborative filtering
is a large amount items rating by users.
The system provides a solution to the problem of
how to choose a pharmacy in the presence of an
overwhelming amount of information. This system
implements as Recommender System for Pharmacy
Shop by using Item-Based Collaborative Approach.
