Abstract: 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.

Key words: knowledge graph, bidirectional recurrent neural network, attention mechanism, interpretability, cold start