Session-based recommendation intends to predict next purchased items based on anonymous behavior sequences. Numerous economic studies have revealed that item price is a key factor influencing user purchase decisions. Unfortunately, existing methods for session-based recommendation only aim at capturing user interest preference, while ignoring user price preference. Actually, there are primarily two challenges preventing us from accessing price preference. Firstly, the price preference is highly associated to various item features (i.e., category and brand), which asks us to mine price preference from heterogeneous information. Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling. To handle above challenges, we propose a novel approach Bi-Preference Learning Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation. Specifically, the customized heterogeneous hypergraph networks with a triple-level convolution are devised to capture user price and interest preference from heterogeneous features of items. Besides, we develop a Bi-Preference Learning schema to explore mutual relations between price and interest preference and collectively learn these two preferences under the multi-task learning architecture. Extensive experiments on multiple public datasets confirm the superiority of BiPNet over competitive baselines. Additional research also supports the notion that the price is crucial for the task.

翻译：基于会话的推荐旨在根据匿名行为序列预测用户下一个购买的商品。大量经济研究表明，商品价格是影响用户购买决策的关键因素。然而，现有基于会话的推荐方法仅聚焦于捕捉用户兴趣偏好，忽略了用户价格偏好。实际上，获取价格偏好面临两大挑战：首先，价格偏好与多种商品特征（如类别和品牌）高度关联，需要我们从异构信息中挖掘价格偏好；其次，价格偏好与兴趣偏好相互依赖、共同决定用户选择，要求我们在意图建模中联合考虑两者。为应对上述挑战，我们提出了一种新的方法——双偏好学习异构超图网络（BiPNet）用于基于会话的推荐。具体而言，我们设计了具有三级卷积的自定义异构超图网络，从商品的异构特征中捕捉用户的价格偏好与兴趣偏好。此外，我们开发了双偏好学习范式来探索价格偏好与兴趣偏好之间的相互关联，并在多任务学习架构下共同学习这两种偏好。在多个公开数据集上的大量实验证实了BiPNet相比竞争基线的优越性。附加研究进一步支持了价格对该任务至关重要的观点。