基于知识图谱和Bi-LSTM的推荐算法

目前现有基于模型的推荐算法多是将评分数据输入到深度学习模型中进行训练，得出推荐结果。其缺陷在于无法对预测结果进行可解释性分析。除此之外，无法有效地解决算法的冷启动问题。因此，本文提出一种基于知识图谱和Bi-LSTM的推荐算法，来有效解决算法的可解释性和冷启动问题。首先将获取到的数据集进行预处理，生成预编码向量，根据数据集结点的连接性，构建专业领域知识图谱。其次利用知识图谱的元路径提取技术获取到多条用户-物品路径信息，将其输入到Bi-LSTM中，在路径经过的各结点处加入一层注意力机制，目的是为了模型能够有效地获取到较远结点的信息。最后将多条路径的训练结果输入到平均池化层中，用以区分不同路径的重要程度，利用交叉熵损失函数对模型进行训练，从而得出预测结果。实验结果表明，与传统基于循环神经网络模型的推荐算法相比，该算法可有效地提升算法的可解释性以及预测准确性，并缓解算法的冷启动问题。

Recommendation Algorithm Based on Knowledge Graph and Bi-LSTM

At present, most of the existing model-based recommendation algorithms input the score data into the deep learning model for training to get the recommendation results. Its defect is that it is unable to analyze the interpretability of the prediction results. In addition, the algorithm can not effectively solve the cold start problem. Therefore, this paper proposes a recommendation algorithm based on knowledge map and Bi-LSTM to effectively solve the problem of interpretability and cold start of the algorithm. Firstly, the data set is preprocessed to generate precoding vector. According to the connectivity of data aggregation points, the domain knowledge map is constructed. Secondly, the meta path extraction technology of knowledge map is used to obtain multiple user item path information, which is input into Bi-LSTM. A layer of attention mechanism is added to each node of the path, so that the model could effectively obtain the information of remote nodes. Finally, the training results of multiple paths are input into the average pooling layer to distinguish the importance of different paths. The cross-entropy loss function is used to train the model and the prediction results are obtained. The experimental results show that, compared with the traditional recommendation algorithm based on the cyclic neural network model, this algorithm can effectively improve the interpretability and prediction accuracy of the algorithm, and alleviate the cold start problem of the algorithm.