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 multi-objective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.

Cloud computing became so important due to virtualization and IT systems in this decade. It has introduced as a distributed and heterogeneous computing pattern to sharing resources. Task Scheduling is necessary to make high performance heterogeneous computing. The optimization of related parameters, and using heuristic and meta-heuristic algorithms can lead to a reduction of the search space complexity and execution time. So, several studies have tried using a variety of algorithms to solve this issue and improve relative efficiency in their environments. This paper considered examines existing heuristic task scheduling algorithms. First, the concepts of scheduling, the layer of cloud computing, especially scheduling concept in the SaaS and PaaS layer, the main limits for improving the quality of service, evaluation methods of algorithms and applied tools for evaluating these ideas and practical experimental used methods were discussed and compared. Finally, future works in this area were also concluded and a summary of this article is presented in the form of a mind map.