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. First, 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. Second, 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.

基于会话的推荐旨在根据匿名行为序列预测下一个购买的商品。大量经济学研究表明，商品价格是影响用户购买决策的关键因素。不幸的是，现有的基于会话的推荐方法仅旨在捕获用户的兴趣偏好，而忽略了用户的价格偏好。实际上，主要有两个挑战阻碍我们获得价格优惠。首先，价格偏好与各种商品特征（即类别和品牌）高度相关，这要求我们从异构信息中挖掘价格偏好。其次，价格偏好和兴趣偏好是相互依存的，共同决定用户的选择，因此我们需要在意图建模时共同考虑价格偏好和兴趣偏好。为了应对上述挑战，我们提出了一种用于基于会话的推荐的双偏好学习异构超图网络（BiPNet）的新方法。具体来说，设计了具有三级卷积的定制异构超图网络，以从项目的异构特征中捕获用户价格和兴趣偏好。此外，我们开发了一种双偏好学习模式来探索价格和兴趣偏好之间的相互关系，并在多任务学习架构下共同学习这两种偏好。对多个公共数据集的广泛实验证实了 BiPNet 相对于竞争基线的优越性。其他研究也支持价格对于任务至关重要的观点。