  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
