Recommender System for Pharmacy Shop By using Item-Based Collaborative Approach

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.