Intention-aware neural networks with session disentanglement for noise filtering in session-based recommendation

Session-based recommendation is a significant and practical approach in predicting the next action of anonymous users within a recommendation system. However, accurate recommendations remain challenging due to limited information. Recently, many works based on neural networks have been proposed to address this task. Nevertheless, these works tend to focus solely on modeling item relationships while neglecting the importance of sessions and exhibiting suboptimal performance in handling noise items within current sessions. To address these issues, this paper proposes an Intention-Aware Neural Networks with Session Disentanglement (IANNSD) that incorporates session modeling and user intent as key factors. Specifically, in the local relationship encoder (LRE), we compute the similarity between the current session and its neighboring items to alleviate the impact of noise neighbor items on recommendation accuracy. In the global relationship encoder (GRE), sessions serve as a constraint for refining the intent distribution of each item, and a highway network is utilized to optimize the outputs of GRE. Additionally, we design a label optimization module to assist model training. Extensive experiments are carried out on three real datasets, and the experimental results demonstrate that IANNSD surpasses state-of-the-art models in session-based recommendation performance.