Recommendation systems aim to predict users interests and recommend items most likely to interest them. In this paper, we propose a new intelligent recommender system that combines collaborative filtering (CF) with the popular unsupervised machine learning algorithm K-means clustering. Also, we use certain user demographic attributes such as the gender and age to create segmented user profiles, when items (movies) are clustered by genre attributes using K-means and users are classified based on the preference of items and the genres they prefer to watch. To recommend items to an active user, Collaborative Filtering approach then is applied to the cluster where the user belongs. Following the experimentation for well known movies, we show that the proposed system satisfies the predictability of the CF algorithm in GroupLens. In addition, our proposed system improves the performance and time response speed of the traditional collaborative Filtering technique and the Content-Based technique too.

推荐系统旨在预测用户的兴趣并推荐最可能使他们感兴趣的项目。在本文中，我们提出了一种新的智能推荐系统，该系统将协作过滤（CF）与流行的无监督机器学习算法K均值聚类相结合。此外，当使用K均值按流派属性将商品（电影）归类，并且根据商品的偏好和他们喜欢的流派对用户进行分类时，我们使用某些用户人口统计属性（例如性别和年龄）来创建细分的用户个人资料看。为了向活动用户推荐项目，然后将“协作筛选”方法应用于该用户所属的群集。通过对著名电影的实验，我们证明了所提出的系统满足GroupLens中CF算法的可预测性。此外，