Improving graph neural network for session-based recommendation system via non-sequential interactions

Abstract In the absence of user profile information, recommender systems have to only rely on current session information for recommendation. E-commerce sites may use transitions between interactions in each session to improve recommendation. This situation is known as the session-based recommendation. It can be challenging due to the limited information and the uncertain user behavior. Recurrent Neural Networks (RNN) have become the state-of-the-art models for session-based recommendation due to their ability to model long sequences. Although powerful, RNN-based models suffer from learning complex transition between the interactions. To mitigate it, Graph Neural Networks (GNN) have been proposed for session-based recommendation. However, different sequences of interactions may lead to the same outcome especially on E-commerce sites, hence non-sequential interactions between items of the current session may improve the performance of a recommender system. To learn both the sequential and non-sequential transition interactions between the items in the current session, we proposed a GNN based model named GRASER. Specifically, the proposed model first learns the non-sequential and then the sequential transition interactions between the items of the current session using GNN in an end-to-end manner. Extensive experiments were carried out on two datasets: Yoochoose from the RecSys Challenge 2015 and Diginetica from CIKM Cup 2016. The results showed that the proposed model outperforms the other state-of-the-art models by 11% and 10% on MRR@20 on Yoochoose and Diginetica datasets respectively.