Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations normally can only recommend items existing in users' historical sessions. As a result, these GNNs have difficulty recommending items that users have never interacted with (new items), which leads to a phenomenon of information cocoon. Therefore, it is necessary to recommend new items to users. As there is no interaction between new items and users, we cannot include new items when building session graphs for GNN session-based recommender systems. Thus, it is challenging to recommend new items for users when using GNN-based methods. We regard this challenge as '\textbf{G}NN \textbf{S}ession-based \textbf{N}ew \textbf{I}tem \textbf{R}ecommendation (GSNIR)'. To solve this problem, we propose a dual-intent enhanced graph neural network for it. Due to the fact that new items are not tied to historical sessions, the users' intent is difficult to predict. We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item. To solve the challenge that new items cannot be learned by GNNs, inspired by zero-shot learning (ZSL), we infer the new item representation in GNN space by using their attributes. By outputting new item probabilities, which contain recommendation scores of the corresponding items, the new items with higher scores are recommended to users. Experiments on two representative real-world datasets show the superiority of our proposed method. The case study from the real-world verifies interpretability benefits brought by the dual-intent module and the new item reasoning module. The code is available at Github: https://github.com/Ee1s/NirGNN

翻译：推荐系统在电子商务、在线教育和流媒体等多个领域至关重要。当前，基于会话的图神经网络通常只能推荐用户历史会话中存在的物品。因此，这些GNN难以推荐用户从未交互过的新物品，导致信息茧房现象。因此，向用户推荐新物品十分必要。由于新物品与用户之间缺乏交互记录，在为基于会话的推荐系统构建会话图时，无法将新物品纳入其中。因此，使用基于GNN的方法为用户推荐新物品具有挑战性。我们将此挑战称为“基于GNN会话的新物品推荐”。为解决该问题，我们提出一种双意图增强图神经网络。由于新物品与历史会话无关联，用户意图难以预测。我们设计了一个双意图网络，分别从注意力机制和历史数据分布中学习用户意图，从而模拟用户与新物品交互时的决策过程。针对新物品无法被GNN学习的挑战，受零样本学习的启发，我们利用新物品的属性推断其在GNN空间中的表示。通过输出包含相应物品推荐得分的新物品概率，将得分较高的新物品推荐给用户。在两个代表性真实世界数据集上的实验表明了我们方法的优越性。真实世界案例研究验证了双意图模块和新物品推理模块带来的可解释性优势。代码可在GitHub获取：https://github.com/Ee1s/NirGNN