Sequence and graph structure co-awareness via gating mechanism and self-attention for session-based recommendation

Session-based recommendation (SR) is important in online applications for its ability to predict user’s next interactions solely based on ongoing sessions. To recommend proper items at proper time are two key aspects in SR. The sequence of items in a session implies user’s preferences shift, which may give us clues about when the user interacted. The graph constructed based on a session can give latent structural dependencies between items, which may give us clues about which items users interacted with. They complement each other and collaborate to boost the performance of recommendation. Based on the motivation, we propose a novel sequence and graph structure co-awareness session-based recommendation model, namely SeqGo for short. In this model, a gated recurrent unit is employed to obtain sequence information and a gated graph neural network to get graph structure information. A two-stage fusion strategy is built to combine these two types of information to generate the representation of the general interest of users. The gating mechanism is used to calculate the relative importance of sequence and graph structure information. Then, multi-head masked self-attention is applied to assign different weights to different items and ignore irrelevant items. The user's general interest and the last item representing the user's current interest are combined to get the session representation to predict the probability of clicking on the next items. Experiment results on two real-world datasets show that SeqGo outperforms the state-of-the-art baselines.