Applying information foraging theory to understand user interaction with content-based image retrieval

Liu, Haiming; Mulholland, Paul; Song, Dawei; Uren, Victoria; Rüger, Stefan

doi:10.1145/1840784.1840805

Cited by 19 publications

(26 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The three types of questionnaires used 5-point Likert scales and were based on templates used previously [12]. The purpose of the entry questionnaire was the recording of demographic information, such as age, web experience and encyclopedia use.…”

Section: Data Recordedmentioning

confidence: 99%

Looking for genre

Clark

Ruthven

Holt

et al. 2012

Proceedings of the 4th Information Interaction in Context Symposium

Self Cite

View full text Add to dashboard Cite

This paper reports on our task-based observational, logged, questionnaire study and analysis of ocular behavior pertaining to the interaction of structural features of text in Wikipedia using eye tracking. We set natural and realistic tasks searching Wikipedia online focusing on examining which features and strategies (skimming or scanning) were the most important for the participants to complete their tasks. Our research, carried out on a group of 30 participants, highlighted their interactions with the structural areas within Wikipedia articles, the visual cues and features perceived during the searching of the Wiki text. We collected questionnaire and ocular behavior (fixation metrics) data to highlight the ways in which people view the features in the articles. We found that our participants' extensively interacted with layout features, such as tables, titles, bullet lists, contents lists, information boxes, and references. The eye tracking results showed that participants used the format and layout features and they also highlighted them as important. They were able to navigate to useful information consistently, and they were an effective means of locating relevant information for the completion of their tasks with some success. This work presents results which contribute to the long-term goals of studying the features for genre and theoretical perception research.

show abstract

Section: Data Recordedmentioning

confidence: 99%

Looking for genre

Clark

Ruthven

Holt

et al. 2012

Proceedings of the 4th Information Interaction in Context Symposium

Self Cite

View full text Add to dashboard Cite

show abstract

“…This paper proposes a methodology for applying an Information Foraging Theory (IFT) based user classification model [4] to classify users into different characteristic groups in order to understand different search preferences of the different user groups for providing them with personalised search experiences. The findings from a systematically structured analysis of the users' interaction data, collected from an extensive empirical user study, further validate the user model and establish the preferences of the different user types for the design and development of personalised content-based image retrieval (CBIR) systems.…”

Section: Introductionmentioning

confidence: 99%

“…The users can be very different when they use a search system: some people are patient, but some are not; some people frequently change their mind on what they are looking for, but some do not; some people are easily satisfied with the result they get after a few rounds of search, but some are not [10]. It is also important to note that the user types are usually implicit, which can be reflected and characterised by the users' search behaviours during the interaction with the system [4]. Learning more useful information from users through user interaction data becomes vital to improve search system personalisation and better engage users during the search process [3].…”

Section: Introductionmentioning

confidence: 99%

“…Extensive work has been done to provide personalised search for improving users' overall search effectiveness and experiences [1,4,9,11,13,14,15]. However, there still exist gaps and challenges on various aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The user classification model developed by Liu et al [4] based on Information Foraging Theory (IFT) is the only theory-based user model that we know which is developed for CBIR search using a deductive approach. We consider this model is a suitable candidate to be applied to interactive CBIR search systems for developing personalised search.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of Applying a Theory-Based User Classification Model to Inform Personalised Content-Based Image Retrieval System Design

2016

Self Cite

View full text Add to dashboard Cite

To better understand users and create more personalised search experiences, a number of user models have been developed, usually based on different theories or empirical data study. After developing the user models, it is important to effectively utilise them in the design, development and evaluation of search systems to improve users' overall search experiences. However there is a lack of research has been done on the utilisation of the user models especially theory-based models, because of the challenges on the utilization methodologies when applying the model to different search systems. This paper explores and states how to apply an Information Foraging Theory (IFT) based user classification model called ISE to effectively identify user's search characteristics and create user groups, based on an empirically-driven methodology for content-based image retrieval (CBIR) systems and how the preferences of different user types inform the personalized design of the CBIR systems.

show abstract

Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

Jaiswal

Liu

Frommholz

2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Query Auto-completion (QAC) is a prominently used feature in search engines, where user interaction with such explicit feature is facilitated by the possible automatic suggestion of queries based on a prefix typed by the user. Existing QAC models have pursued a little on user interaction and cannot capture a user's information need (IN) context. In this work, we devise a new task of QAC applied on an image for estimating patch (one of the key components of Information Foraging Theory) probabilities for query suggestion. Our work supports query completion by extending a user query prefix (one or two characters) to a complete query utilising a foraging-based probabilistic patch selection model. We present iBERT, to fine-tune the BERT (Bidirectional Encoder Representations from Transformers) model, which leverages combined textual-image queries for a solution to image QAC by computing probabilities of a large set of image patches. The reflected patch probabilities are used for selection while being agnostic to changing information need or contextual mechanisms. Experimental results show that query autocompletion using both natural language queries and images is more effective than using only language-level queries. Also, our fine-tuned iBERT model allows to efficiently rank patches in the image.

show abstract

Applying information foraging theory to understand user interaction with content-based image retrieval

Cited by 19 publications

References 17 publications

Looking for genre

Looking for genre

Exploration of Applying a Theory-Based User Classification Model to Inform Personalised Content-Based Image Retrieval System Design

Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion

Contact Info

Product

Resources

About