Denoising dual sparse graph attention model for session-based recommendation

Nowadays, session-based recommendation plays an increasingly important role in the e-commerce field, which predicts the item that a user may click next time based on the sequence of user clicks. However, in real-world scenarios, due to various factors, there is noise in the process of user click behavior. For example, an unexpected click may not be the user's true intention and thus affect the user's behavior prediction. The current denoising methods have the following challenges: denoising directly from a single user's click sequence is not sufficient, the impact of unexpected clicks is not fully considered, and the additional information of other users is not fully utilized. To address these challenges, a new denoising dual sparse graph attention model for session-based recommendation abbreviated as DDSG, which not only considers the information in the current session, but also utilizes the information outside the current session. In the current session, this paper uses position encoding and gated neural networks that are biased towards frequency information to obtain the initial embedding, and uses the self-attention mechanism to model the target representation. In terms of denoising, we first iteratively denoise the representation obtained in the session, and perform sparse self-attention denoising on the session representation based on the target representation. Outside the current session, we take other sessions with similar interests to the current session to enhance the current session. Finally, the user's next click item is predicted by combining internal and extra information of the session. Experiments on three e-commerce datasets demonstrate our model exceeded the optimal SOTA model by 107%, achieving the highest performance and verifying the effectiveness of our model.