论文标题
使用深度学习和图形分析方法的推荐系统
Recommendation system using a deep learning and graph analysis approach
论文作者
论文摘要
当用户连接到互联网以满足他的需求时，他经常遇到大量相关信息。推荐系统是大量过滤信息并提供用户发现它们令人满意且有趣的项目的技术。机器学习方法的进步，尤其是深度学习，导致了推荐系统的巨大成就，尽管这些系统仍然遭受了诸如寒冷和稀疏问题之类的挑战。为了解决这些问题，通常使用上下文信息，例如用户通信网络。在本文中，我们提出了一种基于基质分解和图形分析方法的新型推荐方法。此外，我们利用深层自动编码器来初始化用户和项目潜在因素，而深层嵌入方法会从用户信任图中收集用户的潜在因素。提出的方法在两个标准数据集上实现。实验结果和比较表明，所提出的方法优于现有的最新建议方法。我们的方法表现优于其他比较方法，并取得了重大改进。
When a user connects to the Internet to fulfill his needs, he often encounters a huge amount of related information. Recommender systems are the techniques for massively filtering information and offering the items that users find them satisfying and interesting. The advances in machine learning methods, especially deep learning, have led to great achievements in recommender systems, although these systems still suffer from challenges such as cold-start and sparsity problems. To solve these problems, context information such as user communication network is usually used. In this paper, we have proposed a novel recommendation method based on Matrix Factorization and graph analysis methods. In addition, we leverage deep Autoencoders to initialize users and items latent factors, and deep embedding method gathers users' latent factors from the user trust graph. The proposed method is implemented on two standard datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing state-of-the-art recommendation methods. Our approach outperforms other comparative methods and achieves great improvements.
