Understanding user intentions is crucial for enhancing product recommendations, navigation suggestions, and query reformulations. However, user intentions can be complex, involving multiple sessions and attribute requirements connected by logical operators such as And, Or, and Not. For example, a user may search for Nike or Adidas running shoes across various sessions, with a preference for the color purple. In another case, a user may have purchased a mattress in a previous session and is now seeking a corresponding bed frame without intending to buy another mattress. Prior research on session understanding has not sufficiently addressed how to make product or attribute recommendations for such complex intentions. In this paper, we introduce the task of logical session complex query answering, where sessions are treated as hyperedges of items, and we formulate the problem of complex intention understanding as a task of logical session complex queries answering (LS-CQA) on an aggregated hypergraph of sessions, items, and attributes. The proposed task is a special type of complex query answering task with sessions as ordered hyperedges. We also propose a new model, the Logical Session Graph Transformer (LSGT), which captures interactions among items across different sessions and their logical connections using a transformer structure. We analyze the expressiveness of LSGT and prove the permutation invariance of the inputs for the logical operators. We evaluate LSGT on three datasets and demonstrate that it achieves state-of-the-art results.

翻译：理解用户意图对于提升产品推荐、导航建议和查询重构至关重要。然而，用户意图可能较为复杂，涉及多个会话以及由逻辑运算符（如And、Or和Not）连接的属性需求。例如，用户可能在不同会话中搜索耐克或阿迪达斯跑鞋，并偏好紫色。另一种情况是，用户曾在之前会话中购买床垫，当前正寻找对应的床架，但无意再次购买床垫。以往关于会话理解的研究未充分解决如何针对此类复杂意图进行产品或属性推荐的问题。本文提出了逻辑会话复杂查询回答任务，其中会话被视为物品的超边，并将复杂意图理解问题形式化为在会话、物品和属性聚合超图上进行逻辑会话复杂查询回答（LS-CQA）的任务。该任务是一种特殊的复杂查询回答任务，会话作为有序超边。我们还提出了一种新模型——逻辑会话图Transformer（LSGT），该模型利用Transformer结构捕捉跨不同会话的物品交互及其逻辑连接。我们分析了LSGT的表达能力，并证明了逻辑运算符输入置换不变性。在三个数据集上的评估表明，LSGT取得了最先进的结果。