Session-based recommendation aims to predict items that an anonymous user
would like to purchase based on her short behavior sequence. The current
approaches towards session-based recommendation only focus on modeling users'
interest preferences, while they all ignore a key attribute of an item, i.e.,
the price. Many marketing studies have shown that the price factor
significantly influences users' behaviors and the purchase decisions of users
are determined by both price and interest preferences simultaneously. However,
it is nontrivial to incorporate price preferences for session-based
recommendation. Firstly, it is hard to handle heterogeneous information from
various features of items to capture users' price preferences. Secondly, it is
difficult to model the complex relations between price and interest preferences
in determining user choices.
To address the above challenges, we propose a novel method Co-guided
Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
Towards the first challenge, we devise a heterogeneous hypergraph to represent
heterogeneous information and rich relations among them. A dual-channel
aggregating mechanism is then designed to aggregate various information in the
heterogeneous hypergraph. After that, we extract users' price preferences and
interest preferences via attention layers. As to the second challenge, a
co-guided learning scheme is designed to model the relations between price and
interest preferences and enhance the learning of each other. Finally, we
predict user actions based on item features and users' price and interest
preferences. Extensive experiments on three real-world datasets demonstrate the
effectiveness of the proposed CoHHN. Further analysis reveals the significance
of price for session-based recommendation.

提出了一种名为 Co-guided Heterogeneous Hypergraph Network (CoHHN) 的方法，用于解决会话式推荐中的价格因素问题。它使用异质超图表示异质信息和富有的关系，并使用注意力层提取出用户的价格偏好和兴趣偏好。此外，该方法通过设计了一个共同指导的学习方案，来模拟价格和兴趣偏好之间的复杂关系，最终通过对项目特征和用户价格和兴趣偏好的预测来实现会话式推荐。