Abstract: 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.

Key words: sequence recommendation, recurrent neural network, knowledge graph, convolutional neural network, attention mechanism