Recommender systems that possess adequate information about users and analyze their information, are capable of offering appropriate items to customers. Collaborative filtering method is one of the popular recommender system approaches that produces the best suggestions by identifying similar users or items based on their previous transactions. The low accuracy of suggestions is one of the major concerns in the collaborative filtering method. Several methods have been introduced to enhance the accuracy of this method through the discovering association rules and using evolutionary algorithms such as particle swarm optimization. However, their runtime performance does not satisfy this need, thus this article proposes an efficient method of producing cred associations rules with higher performances based on a genetic algorithm. Evaluations were performed on the data set of MovieLens. The parameters of the assessment are: run time, the average of quality rules, recall, precision, accuracy and F1-measurement. The experimental evaluation of a system based on our algorithm outperforms show than the performance of the multiobjective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.

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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 using associative rules for recommendation systems to improve accuracy but runtime of rule-based recommendation systems is high and cannot be used in the 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 the 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. 1-IntroductionOnline business success highly relies on the ability to present personal goods, services, and information items to the potential customers. This result in willigness toward recommender systems. Through statistical methods and knoweldge discovery, these systems present services to the customers [1, 2]. Collaborating filtering system is one recommender system presents recommendation through detecting similar users based on enter date and previous transactions [3]. Collaborating filtering based recommender systems have many challenges such as recommend generation speed, database sparsity, scalability, recommends utility and so on. There have been great attempts for overcoming the collaborating filtering problems and these Attempts resulted in the high quality recommender generation. The present study reviews the previous studies in this area and examines the steps and resulted findings.Recommender systems define the information systems able to analyze the previous behaviors and present the suggestions for the current issues. In other word, recommender systems try to guess the user`s thinking through his similar behavior or other similar users in order to get the best case most appropriately to the user`s taste [1, 2].There are many types of recommender systems such as:• Content-Based [4]: the working method of content filtering, based on item content analysis and trying to understand the discipline among them for generating the recommendation.

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Cloud computing is considered a pattern for distributed and heterogeneous computing derived from many resources, and requests aim to share resources. Recently, cloud computing is graded among the top best technologies globally, which must be scheduled favorably to maximize providers’ profit and improve service quality for their customers. Scheduling specifies how users’ requests are assigned to virtual machines, and it plays a vital role in the efficiency and capability of the system. Its objective is to have a throughput or complete jobs in minimum time and the highest standard. Scheduling jobs in heterogeneous distributed systems is an NP-hard polynomial indecisive problem that is not solvable in polynomial time for real-time scheduling. The time complexity of jobs is growing exponentially, and this problem has a considerable effect on the quality of cloud services and providers’ efficiencies. The optimization of scheduling-related parameters using heuristic and meta-heuristic algorithms can reduce the search space complexity and execution time. This study intends to represent a fitness function to minimize time and cost parameters. The proposed method uses a multi-purposed weighted genetic algorithm that provides six basic parameters: utility, task execution cost, response time, wait time, Makespan, and throughput to provide comprehensive optimization. The proposed approach improved response and wait times, throughput, Makespan, and utility 16, 9, 7, 8 percentages, respectively, by only a one cost unit reduction, which is dispensable. As a result, both providers and users will experience better services. The statistical tests show that the achieved improvement is valid for 94% of experiments.