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.

通过引入逻辑会话复杂查询应答任务，以及提出逻辑会话图变换器（LSGT）模型，这篇论文研究了用户意图理解的复杂性，并证明了 LSGT 模型在此任务上取得了最先进的结果。