The changing preferences of users towards items trigger the emergence of session-based recommender systems (SBRSs), which aim to model the dynamic preferences of users for next-item recommendations. However, most of the existing studies on SBRSs are based on long sessions only for recommendations, ignoring short sessions, though short sessions, in fact, account for a large proportion in most of the real-world datasets. As a result, the applicability of existing SBRSs solutions is greatly reduced. In a short session, quite limited contextual information is available, making the next-item recommendation very challenging. To this end, in this paper, inspired by the success of few-shot learning (FSL) in effectively learning a model with limited instances, we formulate the next-item recommendation as an FSL problem. Accordingly, following the basic idea of a representative approach for FSL, i.e., meta-learning, we devise an effective SBRS called INter-SEssion collaborative Recommender netTwork (INSERT) for next-item recommendations in short sessions. With the carefully devised local module and global module, INSERT is able to learn an optimal preference representation of the current user in a given short session. In particular, in the global module, a similar session retrieval network (SSRN) is designed to find out the sessions similar to the current short session from the historical sessions of both the current user and other users, respectively. The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed INSERT over the state-of-the-art SBRSs when making next-item recommendations in short sessions.

中文翻译：

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短期会议中的下一项建议

用户对项目的偏好变化引发了基于会话的推荐系统（SBRS）的出现，该系统旨在模拟用户对下一个项目推荐的动态偏好。然而，现有的关于SBRSs的研究大多基于长会话仅用于推荐，而忽略了短会话，虽然短会话实际上在大多数现实世界的数据集中占了很大比例。因此，现有 SBRS 解决方案的适用性大大降低。在一个简短的会话中，可用的上下文信息非常有限，这使得下一个项目的推荐非常具有挑战性。为此，在本文中，受到小样本学习 (FSL) 在有效学习有限实例模型方面的成功启发，我们将下一项推荐制定为 FSL 问题。因此，遵循 FSL 代表性方法的基本思想，即元学习，我们设计了一种有效的 SBRS，称为跨 SEssion 协作推荐网络（INSERT），用于短会话中的下一个项目推荐。通过精心设计的本地模块和全局模块，INSERT 能够在给定的短会话中学习当前用户的最佳偏好表示。特别地，在全局模块中，设计了一个相似会话检索网络（SSRN），分别从当前用户和其他用户的历史会话中找出与当前短会话相似的会话。然后利用获得的相似会话来补充和优化本地模块从当前短会话中学到的偏好表示，以便在这个短会话中更准确地推荐下一个项目。在两个真实世界的数据集上进行的大量实验证明了我们提出的 INSERT 在短期会议中提出下一个项目建议时优于最先进的 SBRS。