The amount of movie has increased to become more congested; therefore, to find a movie what users are looking for through the existing technologies are very hard. For this reason, the users want a system that can suggest the movie requirement to them and the best technology about these is the recommendation system. However, the most recommendation system is using collaborative filtering methods to predict the needs of the user due to this method gives the most accurate prediction. Today, many researchers are paid attention to develop several methods to improve accuracy rather than using collaborative filtering methods. Hence, to further improve accuracy in the recommendation system, we present the k-clique methodology used to analyze social networks to be the guidance of this system. In this paper, we propose an efficient movie recommendation algorithm based on improved k-clique methods which are the best accuracy of the recommendation system. However, to evaluate the performance; collaborative filtering methods are monitored using the k nearest neighbors, the maximal clique methods, the k-clique methods, and the proposed methods are used to evaluate the MovieLens data. The performance results show that the proposed methods improve more accuracy of the movie recommendation system than any other methods used in this experiment.

中文翻译：

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一种基于改进的k -clique的高效电影推荐算法

电影的数量增加了，变得更加拥挤。因此，要找到电影，用户通过现有技术寻找的东西非常困难。因此，用户想要一个可以向他们建议电影要求的系统，而关于这些的最佳技术就是推荐系统。但是，最推荐的系统是使用协作过滤方法来预测用户的需求，因为此方法给出了最准确的预测。如今，许多研究人员开始注意开发几种提高准确性的方法，而不是使用协作过滤方法。因此，为了进一步改善在推荐系统的准确性，我们目前的ķ-用于分析社交网络的古朴方法作为该系统的指导。在本文中，我们提出了一种基于改进的k -clique方法的高效电影推荐算法，该算法是推荐系统的最佳准确性。但是，要评估性能；使用k个最近邻居，最大集团方法，k个clicli方法监视协作过滤方法，并使用所提出的方法评估MovieLens数据。性能结果表明，所提出的方法比本实验中使用的任何其他方法提高了电影推荐系统的准确性。