Rain or shine? Forecasting search process performance in exploratory search tasks

2015 ◽  
Vol 67 (7) ◽  
pp. 1607-1623 ◽  
Author(s):  
Chirag Shah ◽  
Chathra Hendahewa ◽  
Roberto González-Ibáñez
Keyword(s):  
Search Process ◽  
Exploratory Search ◽  
Process Performance ◽  
Search Tasks

User Interaction with Linked Data

2016 ◽  
Vol 7 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Dhavalkumar Thakker ◽  
Fan Yang-Turner ◽  
Dimoklis Despotakis
Keyword(s):  
Linked Data ◽  
User Study ◽  
User Interaction ◽  
Heterogeneous Data ◽  
User Studies ◽  
Sensor Data ◽  
Exploratory Search ◽  
Search Tasks ◽  
Algorithmic Support ◽  
Future Work

It is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed.

Dissociating central and auditory attention: Is there a shared bottleneck for response selection and auditory search?

2020 ◽  
Vol 73 (10) ◽  
pp. 1564-1574
Author(s):  
Florian Kattner ◽  
Christina B Reimer
Keyword(s):  
Discrimination Task ◽  
Response Selection ◽  
Search Time ◽  
Stimulus Onset ◽  
Auditory Attention ◽  
Search Process ◽  
Capacity Limitations ◽  
Central Attention ◽  
Search Tasks ◽  
Auditory Search

Central and auditory attention are limited in capacity. In dual-tasks, central attention is required to select the appropriate response, but because central attention is limited in capacity, response selection can only be carried out for one task at a time. In auditory search tasks, search time to detect the target sound increases with the number of distractor sounds added to the auditory scene (set sizes), indicating that auditory attention is limited in capacity. Here, we investigated whether central and auditory attention relied on common or distinct capacity limitations using a dual-task paradigm. In two experiments, participants completed a visual choice discrimination task (task 1) together with an auditory search task (task 2), and the two tasks were separated by an experimentally modulated stimulus onset asynchrony (SOA). Analysing auditory search time as a function of SOA and set sizes (locus-of-slack method) revealed that the auditory search process in task 2 was performed after response selection in a visual two-choice discrimination task 1 (Experiment 1), but concurrently with response selection in a visual four-choice discrimination task 1 (Experiment 2). Hence, although response selection in the visual four-choice discrimination task demanded more central attention as compared with response selection in the two-choice discrimination task, the auditory search process was performed in parallel. Distribution analyses of inter-response time further indicated that parallel processing of response selection and auditory search was not influenced by response grouping. Taken together, the two experiments provided evidence that central and auditory attention relied on distinct capacity limitations.

Use of mind mapping in search process to clarify information needs and improve search satisfaction

2021 ◽  
pp. 016555152110580
Author(s):  
Atiyeh Baghestani Tajali ◽  
Azam Sanatjoo ◽  
Hassan Behzadi ◽  
Hamid R Jamali Mahmuei
Keyword(s):  
Information Needs ◽  
Human Mind ◽  
Search Process ◽  
Information Need ◽  
Mind Mapping ◽  
Search Tasks ◽  
Significant Difference ◽  
Internet Information ◽  
Mind Maps ◽  
The Mind

A mind map is an approach to the organisation of the human mind that prepares the ground for thinking. Inspired by the function of the mind in handling a situation, this article reports on an empirical study that evaluated the efficiency of mind map techniques and tools in formulating and refining information needs. The study examined graduate students’ Internet information searching. Two simulated search tasks were completed by participants in two search sessions. The results revealed no statistically significant difference between searching with a mind map and without a mind map, and therefore, no advantage could be found for using a mind map in the search process. Participants were happier with their search session when not using mind maps; mind map might help information need clarification, but it is a barrier to interaction and serendipity retrieval. However, this could be due to the search setting where the mind map had to be used as a separate tool and not an integrated component of the search system. The article also discusses some potential benefits of mind mapping for searching.

scholarly journals Search-logger analyzing exploratory search tasks

2011 ◽  
Author(s):  
Georg Singer ◽  
Ulrich Norbisrath ◽  
Eero Vainikko ◽  
Hannu Kikkas ◽  
Dirk Lewandowski
Keyword(s):  
Exploratory Search ◽  
Search Tasks

Improving the effectiveness of subject facets in library catalogs and beyond: a MARC-based semiautomated approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Andrea Cuna ◽  
Gabriele Angeli
Keyword(s):  
Design Methodology ◽  
Exploratory Search ◽  
Controlled Vocabularies ◽  
Content Type ◽  
Client Server ◽  
Search Tasks ◽  
Facet Analysis ◽  
High Level ◽  
User Friendly ◽  
Practical Implications

PurposeThis paper puts forward a MARC-based semiautomated approach to extracting semantically rich subject facets from general and/or specialized controlled vocabularies for display in topic-oriented faceted catalog interfaces in a way that would better support users' exploratory search tasks.Design/methodology/approachHierarchical faceted subject metadata is extracted from general and/or specialized controlled vocabularies by using standard client/server communication protocols. Rigorous facet analysis, classification and linguistic principles are applied on top of that to ensure faceting accuracy and consistency.FindingsA shallow application of facet analysis and classification, together with poorly organized displays, is one of the major barriers to effective faceted navigation in library, archive and museum catalogs.Research limitations/implicationsThis paper does not deal with Web-scale discovery services.Practical implicationsThis paper offers suggestions that can be used by the technical services departments of libraries, archives and museums in designing and developing more powerful exploratory search interfaces.Originality/valueThis paper addresses the problem of deriving clearly delineated topical facets from existing metadata for display in a user-friendly, high-level topical overview that is meant to encourage a multidimensional exploration of local collections as well as “learning by browsing.”

scholarly journals Supporting exploratory search tasks with interactive user modeling

2013 ◽  
Vol 50 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Tuukka Ruotsalo ◽  
Kumaripaba Athukorala ◽  
Dorota Głowacka ◽  
Ksenia Konyushkova ◽  
Antti Oulasvirta ◽  
...  
Keyword(s):  
User Modeling ◽  
Exploratory Search ◽  
Search Tasks ◽  
Interactive User

scholarly journals Modelling search and stopping in interactive information retrieval

2019 ◽  
Vol 53 (1) ◽  
pp. 40-41
Author(s):  
David Maxwell
Keyword(s):  
Information Retrieval ◽  
Real World ◽  
Search Process ◽  
Decision Point ◽  
Search Tasks ◽  
Complex Picture ◽  
And Performance ◽  
High Level ◽  
The University ◽  
Relevant Material

Searching for information when using a computerised retrieval system is a complex and inherently interactive process. Individuals during a search session may issue multiple queries, and examine a varying number of result summaries and documents per query. Searchers must also decide when to stop assessing content for relevance - or decide when to stop their search session altogether. Despite being such a fundamental activity, only a limited number of studies have explored stopping behaviours in detail, with a majority reporting that searchers stop because they decide that what they have found feels " good enough ". Notwithstanding the limited exploration of stopping during search, the phenomenon is central to the study of Information Retrieval, playing a role in the models and measures that we employ. However, the current de facto assumption considers that searchers will examine k documents - examining up to a fixed depth. In this thesis, we examine searcher stopping behaviours under a number of different search contexts. We conduct and report on two user studies, examining how result summary lengths and a variation of search tasks and goals affect such behaviours. Interaction data from these studies are then used to ground extensive simulations of interaction , exploring a number of different stopping heuristics (operationalised as twelve stopping strategies). We consider how well the proposed strategies perform and match up with real-world stopping behaviours. As part of our contribution, we also propose the Complex Searcher Model , a high-level conceptual searcher model that encodes stopping behaviours at different points throughout the search process (see Figure 1 below). Within the Complex Searcher Model, we also propose a new results page stopping decision point. From this new stopping decision point, searchers can obtain an impression of the page before deciding to enter or abandon it. Results presented and discussed demonstrate that searchers employ a range of different stopping strategies, with no strategy standing out in terms of performance and approximations offered. Stopping behaviours are clearly not fixed, but are rather adaptive in nature. This complex picture reinforces the idea that modelling stopping behaviour is difficult. However, simplistic stopping strategies do offer good performance and approximations, such as the frustration -based stopping strategy. This strategy considers a searcher's tolerance to non-relevance. We also find that combination strategies - such as those combining a searcher's satisfaction with finding relevant material, and their frustration towards observing non-relevant material - also consistently offer good approximations and performance. In addition, we also demonstrate that the inclusion of the additional stopping decision point within the Complex Searcher Model provides significant improvements to performance over our baseline implementation. It also offers improvements to the approximations of real-world searcher stopping behaviours. This work motivates a revision of how we currently model the search process and demonstrates that different stopping heuristics need to be considered within the models and measures that we use in Information Retrieval. Measures should be reformed according to the stopping behaviours of searchers. A number of potential avenues for future exploration can also be considered, such as modelling the stopping behaviours of searchers individually (rather than as a population), and to explore and consider a wider variety of different stopping heuristics under different search contexts. Despite the inherently difficult task that understanding and modelling the stopping behaviours of searchers represents, potential benefits of further exploration in this area will undoubtedly aid the searchers of future retrieval systems - with further work bringing about improved interfaces and experiences. Doctoral Supervisor Dr Leif Azzopardi (University of Strathclyde, Scotland) Examination Committee Professor Iadh Ounis (University of Glasgow, Scotland) and Dr Suzan Verberne (Leiden University, The Netherlands). Thanks to both of you for your insightful and fair questioning during the defence! Availability This thesis is available to download from http://www.dmax.org.uk/thesis/, or the University of Glasgow's Enlighten repository - see http://theses.gla.ac.uk/41132/. A Quick Thank You Five years of hard work has got me to the point at which I can now submit the abstract of my doctoral thesis to the SIGIR Forum. There have been plenty of ups and downs, but I'm super pleased with the result! Even though there is only a single name on the front cover of this thesis, there are many people who have helped me get to where I am today. You all know who you are - from my friends and family, those who granted me so many fantastic opportunities to travel and see the world - and of course, to Leif. Thanks to all of you for confiding your belief and trust in me, even when I may have momentarily lost that belief and trust in myself. This thesis is for you all.

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