In the context of user uncertainty and limited information, predicting user preference is a challenging work in many online services, e.g., e-commerce and media streaming. Recent advances in session-based recommendation mostly focus on mining more available information within the current session. However, those methods ignored the sessions with similar context for the current session, which contains rich collaborative information. Therefore, in this study, we proposed a novel Leveraging Neighborhood Session Information with Dual Attentive Neural Network (LNIDA) for session-based recommendation. Specifically, LNIDA contains two main components, i.e., Current Session Encoder (CSE) and Neighborhood Session Encoder (NSE). The CSE module exploits an item-level attention mechanism to model user’s own information in the current session, and the NSE module further captures neighborhood collaborative information via a session-level attention. Then, a simple co-attention fusion mechanism is used to dynamically combine information from the CSE and NSE. Finally, to verify the performance of LNIDA, we conduct extensive experiments on three benchmark datasets, YOOCHOOSE and DIGINETICA, and the experiment results clearly show the effectiveness of LNIDA. Furthermore, we find out that LNIDA can improve performance when modeling the current session information and the neighborhood session information simultaneously.

在用户不确定和信息有限的情况下，预测用户喜好是许多在线服务（例如，电子商务和媒体流）中的一项挑战性工作。基于会话的推荐的最新进展主要集中于在当前会话中挖掘更多可用信息。但是，这些方法忽略了当前会话具有相似上下文的会话，该会话包含丰富的协作信息。因此，在这项研究中，我们提出了一种新颖的利用双注意力神经网络（LNIDA）的邻居会话信息来进行基于会话的推荐。具体而言，LNIDA包含两个主要组件，即当前会话编码器（CSE）和邻居会话编码器（NSE）。CSE模块利用项目级别的关注机制在当前会话中对用户自己的信息进行建模，NSE模块还通过会话级关注来捕获社区协作信息。然后，使用简单的共同注意融合机制动态组合来自CSE和NSE的信息。最后，为了验证LNIDA的性能，我们在三个基准数据集YOOCHOOSE和DIGINETICA上进行了广泛的实验，实验结果清楚地表明了LNIDA的有效性。此外，我们发现，当同时对当前会话信息和邻居会话信息进行建模时，LNIDA可以提高性能。我们在三个基准数据集YOOCHOOSE和DIGINETICA上进行了广泛的实验，实验结果清楚地表明了LNIDA的有效性。此外，我们发现，当同时对当前会话信息和邻居会话信息进行建模时，LNIDA可以提高性能。我们在三个基准数据集YOOCHOOSE和DIGINETICA上进行了广泛的实验，实验结果清楚地表明了LNIDA的有效性。此外，我们发现，当同时对当前会话信息和邻居会话信息进行建模时，LNIDA可以提高性能。