Hossein Hatami Varzaneh - Department of Computer Engineering, Faculty of Computer & Electrical Engineering, Kashan Branch, Islamic Azad University,Kashan, Isfahan, Iran
Behzad Soleimani Neysiani - Department of Computer Engineering, Faculty of Computer & Electrical Engineering, University of Kashan, Kashan, Isfahan, Iran
Hassan Ziafat - Young Researcher and Elite Club, Natanz Branch, Islamic Azad University, Natanz, Isfahan, Iran

Recommender systems are designed for offering products to the potential customers. Collaborative Filtering is known as a common way in Recommender systems which offers recommendations made by similar users in the case of entering time and previous transactions. Low accuracy of suggestions due to a database is one of the main concerns about collaborative filtering recommender systems. In this field, numerous researches have been done to use associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and can not be used in real world. So, many researchers suggest using evolutionary algorithms for finding relative best rules at runtime very fast. The present study investigated the works done for producing associative rules with higher speed and quality. In the first step Apriori-based algorithm will be introduced which is used for recommendation systems and then Particle Swarm Optimization algorithm will be described and the issues of these 2 work will be discussed. Studying this research could help to know the issues in this research field and produce suggestions which have higher speed and quality

Recommander Systems; Collaborative Filtering; Association Rule Mining; Multi-Objective Evolutionary Algorithms; Particle Swarm Optimization; Genetic