Recommending Generalized Products in Collaborative Filtering

Abstract

Recommender systems are particularly useful for computer users, here decisions must be normally taken in a short time and the effort required for interacting with the system must be limited as much as possible. The Recommendation systems can help the user to take a decision suggesting those products which best suit his needs and preferences. Recommendation systems have been an important application area and the focus of considerable recent academic and commercial interest. In the classical collaborative filtering recommendation approach, the voting prediction method is based on the computation of the similarity of the active user, to whom a recommendation has to be made, with the other users .Collaborative filtering (CF) describes a variety of processes that automate the interactions of human advisors; a collaborative filter recommends items based upon the opinions of human advisors. In this paper, we implement the recommendation system are the use of Voting by Category method in memory-based collaborative filtering method.