Session-based recommendation systems aim to model users' interests based on their sequential interactions to predict the next item in an ongoing session. In this work, we present a novel approach that can be used in session-based recommendations (SBRs). Our goal is to enhance the prediction accuracy of an existing session-based recommendation model, the SR-GNN model, by introducing an adaptive weighting mechanism applied to the graph neural network (GNN) vectors. This mechanism is designed to incorporate various types of side information obtained through different methods during the study. Items are assigned varying degrees of importance within each session as a result of the weighting mechanism. We hypothesize that this adaptive weighting strategy will contribute to more accurate predictions and thus improve the overall performance of SBRs in different scenarios. The adaptive weighting strategy can be utilized to address the cold start problem in SBRs by dynamically adjusting the importance of items in each session, thus providing better recommendations in cold start situations, such as for new users or newly added items. Our experimental evaluations on the Dressipi dataset demonstrate the effectiveness of the proposed approach compared to traditional models in enhancing the user experience and highlighting its potential to optimize the recommendation results in real-world applications.

翻译：基于会话的推荐系统旨在根据用户的序列交互行为建模其兴趣，以预测正在进行会话中的下一个项目。在本研究中，我们提出了一种可用于基于会话的推荐系统的新方法。我们的目标是通过引入一种应用于图神经网络向量的自适应权重机制，来提升现有基于会话的推荐模型——SR-GNN模型的预测准确性。该机制旨在整合研究过程中通过不同方法获取的多种类型的辅助信息。通过该权重机制，项目在每个会话中被赋予不同程度的重要性。我们假设这种自适应权重策略将有助于实现更准确的预测，从而提升基于会话的推荐系统在不同场景下的整体性能。该自适应权重策略可用于解决基于会话的推荐系统中的冷启动问题，通过动态调整每个会话中项目的重要性，从而在冷启动情境（例如针对新用户或新添加项目）中提供更好的推荐。我们在Dressipi数据集上的实验评估表明，与传统模型相比，所提出的方法在提升用户体验方面具有有效性，并凸显了其在现实应用中优化推荐结果的潜力。