Dynamic Interaction-Driven Intent Evolver with Semantic Probability Distributions

Accurately capturing a user's dynamic search intent based on her/his interactions with the system is crucial for improving the performance of session-based search. Existing methods often require the entire interaction sequence within a session to be recomputed continuously at each interaction step, and the token-level interactions are either captured within an overall transformer structure or simply ignored. As a consequence, the current approaches suffer from an increased computation burden and fall short of accurately capturing the dynamic evolution of user intent. In this paper, we propose a novel representation approach, which treats both search intent and candidate documents as dimension-specific probability distributions of token embedding representations. Based on this representation, we propose an Dynamic Interaction-Driven intent Evolver (DIDE) for dynamically updating the user's search intent throughout a session with a lightweight similarity calculation method for document ranking. Comprehensive experimental results demonstrate that DIDE adeptly captures the dynamic nature of session-based search and significantly outperforms a range of strong baseline models across three different datasets.