Sequential recommendation based on graph transformer

Abstract

Sequential Recommendation (SR) is an important scenario in recommendation tasks. Sequential recommendations model the sequential pattern between item-item or user-item based on a user's recent activity in a time series to predict their next preference. However, existing methods are based only on the conventional Graph Neural Networks (GNN) as a model architecture for adaptive fine-tuning of specific SR tasks. To get better recommendation results, more advanced GNNs can be used as the network architecture of the SR method. This paper introduces graph transformer, a combination of GNN and a good sequential task processing model. Then a cross-sectional comparison is made with the current SR method model and its suitability for application in SR tasks is discussed. The comparison shows that the graph transformer is similar in principle and structure to the current SR models, and requires the addition of some adaptive components to be applied in SR tasks. The superior performance after application can be demonstrated from the results data of the Benchmarking-GNNs and Long-Range Graph Benchmark on the models.