近日，信息检索国际顶级会议(SIGIR 2022)公布了录用论文列表，博士生张晓堃关于会话推荐(session-based recommendation)的研究成果被录取为长文。SIGIR是信息检索与数据挖掘领域的顶级会议，被CCF推荐为A类国际学术会议。这次会议共收到794篇有效长文投稿，仅有161篇长文被录用，录用率约20%。

题目：Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation

摘要：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’ purchase behaviors. Besides, economics research also points out that users’ purchase behaviors are mainly 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.

  会话推荐致力于从匿名用户的短期行为序列中预测其购买行为。现有的会话推荐方法只建模了用户的兴趣偏好，而忽略了商品的一个重要属性，即价格。很多市场研究证明价格因素对用户的购买行为影响很大。而且，经济学研究指出用户的购买行为是由价格偏好和兴趣偏好同时决定的。然而，在会话推荐中引入用户的价格偏好是困难的。首先，我们很难从商品的多种异质信息中捕获用户的价格偏好。其次，我们很难建模价格偏好和兴趣偏好对用户决定的复杂影响。

为了解决以上问题，我们提出了一个新的方法，共导的异质超图神经网络。对于第一个问题，我们设计了一个异质超图，用于表示商品异质信息及其间丰富的关系。在异质超图中，我们设计了一个双通道聚合机制来提取多样的信息。然后，我们使用注意力层抽取用户的价格偏好及兴趣偏好。对于第二个问题，我们设计了一个共导的学习机制，来建模用户价格偏好和兴趣偏好之间的复杂关系并且强化两种偏好之间的信息交互。最后，我们基于商品特征及用户的价格偏好和兴趣偏好预测用户的行为。我们在三个真实数据集上进行了大量的实验，实验证明了我们提出模型的有效性。进一步的分析也揭露了价格因素对会话推荐的重要性。