A Term-Based Methodology for Query Reformulation Understanding
원문 초록
Key to any research involving session search is the understanding of how a user's queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user's information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user's motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.
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Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
Lexical Query Modeling in Session Search
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search
Query-oriented Data Augmentation for Session Search
Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics
Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation
토론
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