Understanding Inter-Session Intentions via Complex Logical Reasoning

• URL: http://arxiv.org/abs/2312.13866v2
• Date: Fri, 14 Jun 2024 07:47:47 GMT
• Title: Understanding Inter-Session Intentions via Complex Logical Reasoning
• Authors: Jiaxin Bai, Chen Luo, Zheng Li, Qingyu Yin, Yangqiu Song,
• Abstract summary: We present the task of logical session complex query answering (LS-CQA) We frame the problem of complex intention understanding as an LS-CQA task on an aggregated hypergraph of sessions, items, and attributes. We introduce a new model, the Logical Session Graph Transformer (LSGT), which captures interactions among items across different sessions and their logical connections.
• Score: 50.199811535229045
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Understanding user intentions is essential for improving 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 instance, a user may search for Nike or Adidas running shoes across various sessions, with a preference for purple. In another example, a user may have purchased a mattress in a previous session and is now looking for a matching bed frame without intending to buy another mattress. Existing research on session understanding has not adequately addressed making product or attribute recommendations for such complex intentions. In this paper, we present the task of logical session complex query answering (LS-CQA), where sessions are treated as hyperedges of items, and we frame the problem of complex intention understanding as an LS-CQA task on an aggregated hypergraph of sessions, items, and attributes. This is a unique complex query answering task with sessions as ordered hyperedges. We also introduce 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. By evaluating LSGT on three datasets, we demonstrate that it achieves state-of-the-art results.

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