While session-based recommender systems (SBRSs) have shown superior recommendation performance, multi-task learning (MTL) has been adopted by SBRSs to enhance their prediction accuracy and generalizability further. Hierarchical MTL (H-MTL) sets a hierarchical structure between prediction tasks and feeds outputs from auxiliary tasks to main tasks. This hierarchy leads to richer input features for main tasks and higher interpretability of predictions, compared to existing MTL frameworks. However, the H-MTL framework has not been investigated in SBRSs yet. In this paper, we propose HierSRec which incorporates the H-MTL architecture into SBRSs. HierSRec encodes a given session with a metadata-aware Transformer and performs next-category prediction (i.e., auxiliary task) with the session encoding. Next, HierSRec conducts next-item prediction (i.e., main task) with the category prediction result and session encoding. For scalable inference, HierSRec creates a compact set of candidate items (e.g., 4% of total items) per test example using the category prediction. Experiments show that HierSRec outperforms existing SBRSs as per next-item prediction accuracy on two session-based recommendation datasets. The accuracy of HierSRec measured with the carefully-curated candidate items aligns with the accuracy of HierSRec calculated with all items, which validates the usefulness of our candidate generation scheme via H-MTL.

翻译：基于会话的推荐系统（SBRSs）已展现出优越的推荐性能，而多任务学习（MTL）被SBRSs采用以进一步提升其预测准确性和泛化能力。分层MTL（H-MTL）在预测任务之间建立分层结构，并将辅助任务的输出馈送到主任务中。与现有MTL框架相比，这种分层结构为主任务提供了更丰富的输入特征，并提高了预测的可解释性。然而，H-MTL框架在SBRSs中尚未得到研究。本文提出HierSRec，将H-MTL架构融入SBRSs。HierSRec利用元数据感知的Transformer对给定会话进行编码，并通过会话编码执行下一类别预测（即辅助任务）。接着，HierSRec利用类别预测结果和会话编码执行下一项目预测（即主任务）。为支持可扩展推理，HierSRec针对每个测试示例，基于类别预测创建紧凑的候选项目集（例如总项目的4%）。实验表明，HierSRec在两个基于会话的推荐数据集上，在下一项目预测准确性方面优于现有SBRSs。通过精心筛选候选项目测得的HierSRec准确性与使用所有项目计算的结果一致，这验证了我们通过H-MTL生成的候选方案的实用性。