This paper introduces a method for improving the association rules that will also help improve the performance of recommender systems. Combining sampling and parallelism in the process, the proposed method, in addition to help perform the process more quickly, better quality rules will be generated. The proposed method and the dataset will be segmented into a number of smaller sections based on clustering items, and each section is separately sampled. Frequent itemsets and association rules in any section of the selected samples will be found and by gathering and analyzing the results, the quality and number of rules will be evaluated. One of the innovations of this article is the method of sampling of the database. Instead of random sampling, information about the best users and items was isolated. After determining the precise amount of support, the authors extracted the frequent and favorable rules from the selected sample.