User basic data (e.g. user gender, user age and user ID, etc.) and item basic data (e.g. item name, item category, etc.) are important side data that can be used to enhance the performance of recommendation algorithms, whereas attempts concerning this issue are still relatively scarce. In this study, a deep neural networks based recommendation algorithm is proposed where user average rating, user basic data (user gender, user age, user occupation, user ID), item basic data (item name, item category, item ID) and item average rating are used. The main idea of the algorithm is to build a regression model for predicting user ratings based on deep neural networks. For this, according to the user data (user average rating and user basic data) and the item data (items basic data and item average rating), a user feature matrix and an item feature matrix are respectively constructed using the four types of neural network layers [i.e., embedding layer (EL), convolution layer (CL), pooling layer (PL) and fully connected layer (FCL)]. Then, based on the obtained user feature matrix and item feature matrix, a user-item feature matrix is further constructed using a FCL. On this basis, a regression model for predicting user ratings can be trained, and a recommendation list can be generated according to the predicted user ratings. To verify the effectiveness of the proposed algorithm, three experiments are conducted using the real data from the MovieLens website. The results of experiments show that the proposed algorithm not only outperforms the state-of-the-art collaborative filtering (CF) recommendation algorithms but also alleviates the data sparsity problem and cold-start problem that would occur when the state-of-the-art CF recommendation algorithms are used.

中文翻译：

---

使用用户和项目基本数据的基于深度神经网络的推荐算法

用户基本数据（例如，用户性别，用户年龄和用户ID等）和项目基本数据（例如，项目名称，项目类别等）是重要的辅助数据，可用于增强推荐算法的性能，而尝试关于这个问题仍然相对稀缺。在这项研究中，提出了一种基于深度神经网络的推荐算法，其中用户平均评分，用户基本数据（用户性别，用户年龄，用户职业，用户ID），商品基本数据（商品名称，商品类别，商品ID）和商品使用平均评分。该算法的主要思想是基于深度神经网络构建用于预测用户收视率的回归模型。为此，根据用户数据（用户平均评分和用户基本数据）和项目数据（项目基本数据和项目平均评分），使用四种类型的神经网络层[即嵌入层（EL），卷积层（CL），池化层（PL）和完全连接层（FCL）]分别构建用户特征矩阵和项目特征矩阵。然后，基于所获得的用户特征矩阵和项目特征矩阵，使用FCL进一步构建用户项目特征矩阵。在此基础上，可以训练用于预测用户评分的回归模型，并可以根据预测的用户评分生成推荐列表。为了验证所提出算法的有效性，使用了MovieLens网站上的真实数据进行了三个实验。