Recommendation systems are the systems thathelp users to select
suitable items from a large collection of items based on their tastes
and interests. Such systems have become one of the most powerful
tools in electronic commerce and social websites
. Nonetheless ,
using these systems in e-commerce websites faces many drawbacks
such as: cold start-up, scalability and sparsity.
In this paper, we present a solution to cold-start-up problem, and
compare between many association rule algorithms to select the
most suitable one to solve the scalability and sparsity problems.
Recommender systems represents a class of systems designed to help individuals deal with information overload or incomplete
information. Such systems help individuals by providing recommendation through the use of various personalization
techniques
. Collaborative filtering is a widely used technique for rating prediction in recommender systems. This paper presents a
method uses preference relations instead of absolute ratings for similarity calculation.
The result indicates that the proposed method outperform the other methods such as the Somers Coefficient.
