Interval-enhanced Graph Transformer solution for session-based recommendation

In many online recommendation services (e.g., multimedia streaming, e-commerce), predicting user's next behavior based on anonymous sessions remains a challenging problem, mainly due to the lack of basic user information and limited behavioral information. The existing typical methods either model user behavior sequences based on RNN or capture potential relationships among items based on GNN. However, these pioneers ignore the importance of different time intervals in the behavior sequence, which implies the user preferences and makes the session sequence more distinguishable. Towards this end, we contribute an Interval -enhanced Graph Transformer (IGT) solution for the session-based recommendation, which takes both item relations and corresponding time intervals into consideration. Specifically, IGT consists of three modules: (i) Interval-enhanced session graph, which constructs all session sequences as session graphs with time intervals; (ii) Graph Transformer, which is embedded with time intervals is adopted to learn the complex interaction information among items. Among them, we design various time interval embedding functions, which can be flexibly injected into the framework; (iii) Preference representation and prediction, which uses an attention network to fuse the user's long-term preferences and short-term preferences to predict the next click. By conducting extensive experiments on the DIGINETICA, YOOCHOOSE and Last.FM three real-world datasets, we validate that IGT outperforms state-of-the-art solutions.

Automated summary

In many online recommendation services, predicting user's next behavior based on anonymous sessions is still a challenging problem. Existing methods either model user behavior sequences using RNN or capture relationships among items using GNN, but they ignore the importance of different time intervals in the behavior sequence. To address this, we propose an Interval-enhanced Graph Transformer (IGT) solution that considers both item relations and corresponding time intervals. Experimental results on real-world datasets demonstrate the superiority of IGT over state-of-the-art solutions.