Understanding Inter-Session Intentions via Complex Logical Reasoning

Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song

Primary Area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)

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Keywords: knowledge graph, complex query answering, session understanding

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Abstract:

Understanding user intentions is crucial for enhancing product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, 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 query answering (LSQA), where sessions are treated as hyperedges of items. We formulate the problem of complex intention understanding as a task of answering logical queries on an aggregated hypergraph of sessions, items, and attributes. 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.

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Submission Number: 2938