Recommendation systems are information filtering tools that present items to users based on their preferences and behavior, for example, suggestions about scientific papers or music a user might like. Based on what we said and with the development of computer science that has started to take an interest in big data and how it is used to discover user interest, we have found a lot of research going on in the area of recommendation and there are powerful systems available. In the unsupervised learning domain, this paper introduces a novel method for creating a hybrid recommender framework that combines Collaborative Filtering with Content Based Approach and Self-Organizing Map neural network technique. By testing our system on a subset of the Movies Database, we demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and precision, as well as improving the efficiency of the traditional Collaborative Filtering methodology.

推荐系统是信息过滤工具，它根据用户的偏好和行为向用户展示项目，例如，关于用户可能喜欢的科学论文或音乐的建议。基于我们所说的，随着计算机科学的发展开始对大数据以及如何使用它来发现用户兴趣感兴趣，我们发现在推荐领域进行了很多研究，并且有强大的可用的系统。在无监督学习领域，本文介绍了一种创建混合推荐框架的新方法，该框架将协作过滤与基于内容的方法和自组织映射神经网络技术相结合。通过在电影数据库的一个子集上测试我们的系统，