Session-based recommendation seeks to forecast the next item a user will be interested in, based on their interaction sequences. Due to limited interaction data, session-based recommendation faces the challenge of limited data availability. Traditional methods enhance feature learning by constructing complex models to generate positive and negative samples. This paper proposes a session-based recommendation model using Single Positive optimization loss and Graph Learning (SPGL) to deal with the problem of data sparsity, high model complexity and weak transferability. SPGL utilizes graph convolutional networks to generate global item representations and batch session representations, effectively capturing intrinsic relationships between items. The use of single positive optimization loss improves uniformity of item representations, thereby enhancing recommendation accuracy. In the intent extractor, SPGL considers the hop count of the adjacency matrix when constructing the directed global graph to fully integrate spatial information. It also takes into account the reverse positional information of items when constructing session representations to incorporate temporal information. Comparative experiments across three benchmark datasets, Tmall, RetailRocket and Diginetica, demonstrate the model's effectiveness. The source code can be accessed on https://github.com/liang-tian-tian/SPGL .

翻译：会话推荐旨在根据用户的交互序列预测其可能感兴趣的下一个项目。由于交互数据有限，会话推荐面临着数据可用性不足的挑战。传统方法通过构建复杂模型生成正负样本来增强特征学习。本文提出一种基于单正优化损失与图学习的会话推荐模型（SPGL），以应对数据稀疏性、模型复杂度高及可迁移性弱的问题。SPGL利用图卷积网络生成全局项目表征和批量会话表征，有效捕捉项目间的内在关联。单正优化损失的使用提升了项目表征的均匀性，从而提高了推荐准确性。在意图提取器中，SPGL构建有向全局图时考虑了邻接矩阵的跳数，以充分融合空间信息；在构建会话表征时兼顾项目的反向位置信息，以纳入时序信息。在Tmall、RetailRocket和Diginetica三个基准数据集上的对比实验验证了模型的有效性。源代码可通过 https://github.com/liang-tian-tian/SPGL 获取。