Factors that influence query reformulations and search performance in health information retrieval: A multilevel modeling approach

Lü, Kun; Joo, Soohyung; Lee, Taehun; Hu, Rong

doi:10.1002/asi.23872

Cited by 6 publications

(5 citation statements)

References 49 publications

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“…Session-aware query reformulation is involved in retrieval-based interactive systems, including dynamic IR systems , multi-turn Question Answering (QA) (Mensio et al, 2018), and dialogue systems (Cui et al, 2019). Several works studied the connections between search sessions, intentions in query reformulation, and search behavior (Lu et al, 2017;Liu et al, 2019b;Tamine et al, 2020). Among the major findings, we particularly mention the following: (1) query reformulation patterns can be observed in search sessions providing insights on the search process characteristics such as underlying search task stage (Tamine et al, 2020;Eickhoff et al, 2014) and success (Odijk et al, 2015); (2) during the session search, system's results often lead to a change in both user's knowledge and the complexity of subsequent queries (Eickhoff et al, 2014;Liu et al, 2019a); (3) user search process runs into sequential phases, specialization, and intent shift.…”

Section: Session-aware Query Reformulationmentioning

confidence: 99%

Exploring the Value of Multi-View Learning for Session-Aware Query Representation

Ortíz¹,

Moreno²,

Hubert³

et al. 2022

Findings of the Association for Computational Linguistics: NAACL 2022

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Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user's query reformulations or system's rankings whereas previous findings show that user's query behavior and knowledge change depending on the system's results, intertwine and affect each other during the completion of a search task. In this paper, we explore the value of multi-view learning for generic and unsupervised session-aware query representation learning. First, single-view query embeddings are obtained in separate spaces from query reformulations and document ranking representations using transformers. Then, we investigate the use of linear (CCA) and non linear (UMAP) multi-view learning methods, to align those spaces with the aim of revealing similarity traits in the multi-view shared space. Experimental evaluation is carried out in a query classification and session-based retrieval downstream tasks using respectively the KDD and TREC session datasets. The results show that multiview learning is an effective and controllable approach for unsupervised learning of generic query representations and can reflect search behavior patterns.

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Section: Session-aware Query Reformulationmentioning

confidence: 99%

Exploring the Value of Multi-View Learning for Session-Aware Query Representation

Ortíz¹,

Moreno²,

Hubert³

et al. 2022

Findings of the Association for Computational Linguistics: NAACL 2022

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“…A 47-year-old man presented to the emergency room with left hypochondrium pain that had been evolving for 24 The task data and humanassessed sub-tasks Task, Task description, expert expected sub-tasks a set of candidate ones. Second, we also evaluate search strategy identification which consists in indicating if an issued query in context (based on previous queries in the session) mainly indicates either an exploration or an exploitation strategy.…”

Section: Problem-solving (Trpmed)mentioning

confidence: 99%

“…A number of previous user studies highlighted the impact of different task characteristics on users' query reformulations [24,34,38,43]. The results mainly revealed that: 1) the domain knowledge of the task doer significantly impacts query term changes; 2) the cognitive complexity of the task (e.g., simple, hierarchical, parallel) has a significant effect on users' query reformulation behavior.…”

Section: Introductionmentioning

confidence: 99%

CoST: An annotated Data Collection for Complex Search

Dosso

Moreno

Chevalier

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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While great progress is made in the area of information access, there are still open issues that involve designing intelligent systems supporting task-based search. Despite the importance of task-based search, the information retrieval and information science communities still feel the lack of open-ended and annotated datasets that enable the evaluation of a number of related facets of search tasks in downstream applications. Existing datasets are either sampled from large-scale logs but provide poor annotations, or sampled from lower-scale user studies but focus on ranked list evaluation. In this work, we present CoST 1 : a novel richly annotated dataset for evaluating complex search tasks, collaboratively designed by researchers from the computer science and cognitive psychology domains, and intended to answer a wide range of research questions dealing with task-based search. CoST includes 5667 queries recorded in 630 task-based sessions that result from a user study involving 70 french native participants who are expert in one among 3 different domains of expertise (computer science, medicine, psychology). Each participant completed 15 tasks with 5 different types of cognitive complexity (fact-finding, exploratory learning, decisionmaking, problem-solving, multicriteria-inferential). In addition to search data (e.g., queries and clicks), CoST provides task and sessionrelated data, task annotations and query annotations. We illustrate possible usages of CoST through the evaluation of query classification models and the understanding of the effect of task complexity and domain on user's search behavior.

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“…Another category of work rather employed lab user studies to understand how different task characteristics impact users' query reformulations [15,17,18,28,31,32]. The results mainly revealed that: (1) the domain knowledge of the task doer significantly impacts query term changes.…”

Section: Query Reformulation Understandingmentioning

confidence: 99%

What Can Task Teach Us About Query Reformulations?

Tamine

Lovón-Melgarejo

Pinel-Sauvagnat

2020

Lecture Notes in Computer Science

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A significant amount of prior research has been devoted to understanding query reformulations. The majority of these works rely on time-based sessions which are sequences of contiguous queries segmented using time threshold on users' activities. However, queries are generally issued by users having in mind a particular task, and time-based sessions unfortunately fail in revealing such tasks. In this paper, we are interested in revealing in which extent time-based sessions vs. task-based sessions represent significantly different background contexts to be used in the perspective of better understanding users' query reformulations. Using insights from large-scale search logs, our findings clearly show that task is an additional relevant search unit that helps better understanding user's query reformulation patterns and predicting the next user's query. The findings from our analyses provide potential implications for model design of task-based search engines.

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Factors that influence query reformulations and search performance in health information retrieval: A multilevel modeling approach

Abstract: which provides a more extended understanding of the relationships among factors that influence query reformulation behavior and search performance. The multilevel modeling used in this study has practical implications for future query reformulation studies.

Cited by 6 publications

References 49 publications

Exploring the Value of Multi-View Learning for Session-Aware Query Representation

Exploring the Value of Multi-View Learning for Session-Aware Query Representation

CoST: An annotated Data Collection for Complex Search

What Can Task Teach Us About Query Reformulations?

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