Session-based recommendation systems must capture implicit user intents from sessions. However, existing models suffer from issues such as item interaction dominance and noisy sessions. We propose a multi-channel recommendation model, including a knowledge graph channel, a session hypergraph channel, and a session line graph channel, to capture information from multiple sources. Our model adaptively removes redundant edges in the knowledge graph channel to reduce noise. Knowledge graph representations cooperate with hypergraph representations for prediction to alleviate item dominance. We also generate in-session attention for denoising. Finally, we maximize mutual information between the hypergraph and line graph channels as an auxiliary task. Experiments demonstrate that our method enhances the accuracy of various recommendations, including e-commerce and multimedia recommendations. We release the code on GitHub for reproducibility.\footnote{https://github.com/hohehohe0509/DSR-HK}

翻译：基于会话的推荐系统必须从会话中捕捉用户的隐含意图。然而，现有模型存在商品交互主导和会话噪声等问题。我们提出了一种多通道推荐模型，包括知识图谱通道、会话超图通道和会话线图通道，以从多个来源捕获信息。我们的模型自适应地移除知识图谱通道中的冗余边以降低噪声。知识图谱表示与超图表示协同进行预测，以缓解商品主导问题。我们还生成了会话内注意力机制以进行去噪。最后，我们将超图通道与线图通道之间的互信息最大化作为辅助任务。实验表明，我们的方法提升了包括电子商务和多媒体推荐在内的多种推荐任务的准确性。我们已在GitHub上开源代码以确保可复现性。\footnote{https://github.com/hohehohe0509/DSR-HK}