Nowadays, most of the websites like Amazon, YouTube and Netflix use collaborative filtering methods to recommend various types of items to users. There are two principal categories of collaborative filtering; memory-based and model-based. The memory-based methods use the users’ similarity measures and have several advantages over the model-based techniques, including being easily explained and easy modeling updates with new ratings and items. However, the memory-based methods’ performance reduces when the data is sparse, and unlike the model-based methods, memory-based methods are not scalable. In this paper, we propose a method that exploit the benefits of both similarity-based and model-based approaches. We address both the reliability and the online updating problems based on a novel user-similarity based method. To calculate the new similarity metric we use the predicted user rating vectors in the autoencoder’s output and apply mutual information to the predicted vectors in order to find similar users. We depict a similarity graph according to the mutual information rate, which is calculated for each pair of users. We implement the proposed method on the Netflix movie recommendation dataset. According to our experiments, the proposed approach has a significant advantage over the other methods, such as the standard autoencoder, the matrix factorization, and the similarity-based methods.

如今，亚马逊、YouTube 和 Netflix 等大多数网站都使用协同过滤方法向用户推荐各种类型的项目。协同过滤有两大类：基于内存和基于模型。基于记忆的方法使用用户的相似性度量，与基于模型的技术相比有几个优点，包括易于解释和易于使用新评级和项目进行建模更新。然而，当数据稀疏时，基于内存的方法的性能会降低，并且与基于模型的方法不同，基于内存的方法不可扩展。在本文中，我们提出了一种利用基于相似性和基于模型的方法的优点的方法。我们基于一种新颖的基于用户相似性的方法来解决可靠性和在线更新问题。为了计算新的相似度度量，我们在自动编码器的输出中使用预测的用户评分向量，并将互信息应用于预测向量以找到相似的用户。我们根据为每对用户计算的互信息率描绘了一个相似图。我们在 Netflix 电影推荐数据集上实现了所提出的方法。根据我们的实验，所提出的方法比其他方法具有显着优势，例如标准自动编码器、矩阵分解和基于相似性的方法。这是为每对用户计算的。我们在 Netflix 电影推荐数据集上实现了所提出的方法。根据我们的实验，所提出的方法比其他方法具有显着优势，例如标准自动编码器、矩阵分解和基于相似性的方法。这是为每对用户计算的。我们在 Netflix 电影推荐数据集上实现了所提出的方法。根据我们的实验，所提出的方法比其他方法具有显着优势，例如标准自动编码器、矩阵分解和基于相似性的方法。