Abstract: The traditional sequential recommendation algorithm based on graph neural network ignores the transformation relationship of items in other user sequences during the graph construction stage. To solve this problem, a sequential recommendation algorithm based on dual-channel light graph convolution is proposed. Firstly, the neighbor user sequence is found for the target user, and the target user sequence and the obtained neighbor sequence are combined into a directed sequence graph, which makes full use of the potential collaborative information between users. Then, the information of the two sequences is propagated through dual-channel light graph convolution. Each channel combines the information of each layer in the form of exponential denominator, and the embedding obtained from the two channels is fused to generate the final item embedding. Finally, the short-term preference is extracted by averaging the last several item embedding, and the long-term preference is extracted by introducing the multi-head self-attention mechanism of squeeze-and-excitation networks, and the final preference of users is obtained by integrating the long-term and short-term preferences. Extensive experiments on two public datasets, Beauty and MovieLens-20M, demonstrate the effectiveness of the proposed algorithm. 

Key words: sequential recommendation, graph construction, exponential denominator, light graph convolution