结合时间特征的协同过滤深度推荐算法

针对推荐算法中的数据稀疏性和冷启动问题，提出了基于卷积神经网络的结合时间特征的协同过滤深度推荐算法（CNN-deep recommend algorithm with time，C-DRAWT）与基于多层感知机的结合时间特征的协同过滤深度推荐算法（MLP-deep recommend algorithm with time，M-DRAWT）。算法进行数据预处理，利用二进制来编码用户与项目的信息，缓解了one-hot编码的书籍稀疏性问题。提取出用户与项目的隐藏特征，将用户和项目的特征融合时间戳特征，分别输入到优化后的卷积神经网络和多层感知机进行，得到最新时刻的推荐项目。两个算法经过基于MovieLens-1M数据集的对比实验验证，得到的F1-Score值平均提高了0.78%，RMSE值平均提高了2.7%。结果表明，该方法能够缓解数据稀疏性和冷启动问题，相比较于之前的模型具有较好的推荐效果。

Collaborative Filtering Deep Recommendation Algorithm Based on Time Feature

Aiming at the problem of data sparsity and cold start in recommendation algorithm, this paper proposes a convolutional neural network based collaborative filtering depth recommendation algorithm（CNN deep recommendation algorithm with time, C-DRAWT） and a multi-layer perceptron based collaborative filtering depth recommendation algorithm（MLP deep recommendation algorithm with time, M-DRAWT）. The algorithm first performs data preprocessing, and uses binary to encode user and project information, which alleviates the sparseness of books in one-hot encoding. Subsequently, the hidden features of the user and the item are extracted, and the features of the user and the item are combined with the timestamp feature, and then input into the optimized convolutional neural network and the multi-layer perceptron respectively, and finally the recommended item at the latest time is obtained. The two algorithms are verified by comparative experiments based on MovieLens-1M data set. The F1 score value and RMSE value are increased by 0.78% and 2.7% respectively. The results show that this method can alleviate the problems of data sparsity and cold start, and has better recommendation effect than the previous model.