基于知识图谱嵌入与多神经网络的序列推荐算法

循环神经网络在序列推荐中占有重要地位，但在推荐中，用户的行为序列远比自然语言处理中的句子或计算机视觉中的图像要复杂得多。单一的循环神经网络结构难以充分地挖掘用户偏好，因此提出一种新型的序列推荐算法，同时考虑序列的时间信息以及内容信息。主要分为2个部分：改进的项目嵌入和序列偏好学习。首先，提出一种融合知识图谱的项目嵌入方法，用于生成高质量的项目向量；其次，提出一种卷积神经网络结合长短时记忆神经网络的序列建模方法。更进一步地提出一个基于注意力的框架，动态地结合用户的兴趣点。在公开数据集MovieLens10M上与传统方法以及现有的同类型方法进行了比较。实验结果表明，所提算法在推荐评价指标平均倒数排名MRR@N以及召回率Recall@N上有显著的提升，验证了该算法的有效性。

A sequence recommendation algorithm based on knowledge graph embedding and multiple neural networks

Recurrent neural networks play an important role in sequence recommendation. However, in recommendation, the user's behavior sequences are far more complicated than the sentences in natural language processing or images in computer vision. A single recurrent neural network structure is difficult to fully mine user preferences, so this paper proposes a new sequence recommendation algorithm that takes into account both the time information and content information of the sequence. This algorithm is mainly divided into two parts: improved item embedding and sequence preference learning. Firstly, an item embedding method that incorporates a knowledge graph is used to generate high-quality item vectors. Secondly, a sequence modeling method combining convolutional neural networks with long-term and short-term memory neural networks is proposed. Furthermore, an attention-based framework is proposed, which dynamically combines user's points of interest. This algorithm is compared with the traditional methods and the existing advanced methods of the same type on the public data set MovieLens10M. The experimental results show that the average reciprocal ranking MRR@N of the recommended evaluation index and the recall rate Recall@N is improved significantly, which verifies the effectiveness of the proposed algorithm.