Effects of relevance criteria and subjective factors on web image searching behaviour

Searching for images is an everyday activity. Nevertheless, even a highly skilled searcher often struggles to find what they are looking for. This article studies the factors that affect users’ online web image search behaviour, investigating (1) the use of criteria in making image relevance judgements and (2) the effect of familiarity, difficulty and satisfaction. The study includes 48 users who performed four online image search tasks using Google Images. Simulated work scenarios, questionnaires and screen capture recordings were used to collect data of their image search behaviour. The results show in judging image relevance, users may apply similar criterion, however, the importance of these criteria depends on the type of image search. Similarly, ratings of users’ perception on subjective aspects of performing image search shows they were task dependent. Users’ perception on subjective aspects of performing image search did not always correspond with their actual search behaviour. Correlation analysis shows that subjective factors cannot be definitively measured by using only one component of search behaviour. Future work includes further analysis on the effects of topic familiarity and satisfaction.

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The Effects on Topic Familiarity on Online Search Behaviour and Use of Relevance Criteria

Lecture Notes in Computer Science - Advances in Information Retrieval ◽

10.1007/11735106_40 ◽

2006 ◽

pp. 456-459 ◽

Cited By ~ 7

Author(s):

Lei Wen ◽

Ian Ruthven ◽

Pia Borlund

Keyword(s):

Online Search ◽

Search Behaviour ◽

Topic Familiarity ◽

Relevance Criteria

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Visual Image Browsing and Exploration (Vibe): User Evaluations of Image Search Tasks

Active Media Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-642-15470-6_44 ◽

2010 ◽

pp. 424-435 ◽

Cited By ~ 12

Author(s):

Grant Strong ◽

Orland Hoeber ◽

Minglun Gong

Keyword(s):

Visual Image ◽

Image Search ◽

Search Tasks ◽

User Evaluations ◽

Image Browsing

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User Interaction with Linked Data

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2016010105 ◽

2016 ◽

Vol 7 (1) ◽

pp. 79-91 ◽

Cited By ~ 3

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.

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Using Noninvasive Brain Measurement to Explore the Psychological Effects of Computer Malfunctions on Users during Human-Computer Interactions

Advances in Human-Computer Interaction ◽

10.1155/2014/101038 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Leanne M. Hirshfield ◽

Philip Bobko ◽

Alex Barelka ◽

Stuart H. Hirshfield ◽

Mathew T. Farrington ◽

...

Keyword(s):

Web Search ◽

Near Infrared ◽

Self Report ◽

Human Computer Interactions ◽

Functional Near Infrared Spectroscopy ◽

Perceived Trust ◽

Search Tasks ◽

Skin Response ◽

Future Work ◽

Computer Interactions

In today’s technologically driven world, there is a need to better understand the ways that common computer malfunctions affect computer users. These malfunctions may have measurable influences on computer user’s cognitive, emotional, and behavioral responses. An experiment was conducted where participants conducted a series of web search tasks while wearing functional near-infrared spectroscopy (fNIRS) and galvanic skin response sensors. Two computer malfunctions were introduced during the sessions which had the potential to influence correlates of user trust and suspicion. Surveys were given after each session to measure user’s perceived emotional state, cognitive load, and perceived trust. Results suggest that fNIRS can be used to measure the different cognitive and emotional responses associated with computer malfunctions. These cognitive and emotional changes were correlated with users’ self-report levels of suspicion and trust, and they in turn suggest future work that further explores the capability of fNIRS for the measurement of user experience during human-computer interactions.

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Order effect in interactive information retrieval evaluation: an empirical study

Journal of Documentation ◽

10.1108/jd-04-2015-0051 ◽

2016 ◽

Vol 72 (2) ◽

pp. 194-213 ◽

Cited By ~ 4

Author(s):

Melanie Landvad Clemmensen ◽

Pia Borlund

Keyword(s):

Information Retrieval ◽

Information Science ◽

Order Effect ◽

Test Design ◽

Performance Parameters ◽

Search Behaviour ◽

Content Type ◽

Interactive Information Retrieval ◽

Search Tasks ◽

Information Retrieval Evaluation

Purpose – The purpose of this paper is to report a study of order effect in interactive information retrieval (IIR) studies. The phenomenon of order effect is well-known, and it is the main reason why searches are permuted (counter-balanced) between test participants in IIR studies. However, the phenomenon is not yet fully understood or investigated in relation to IIR; hence the objective is to increase the knowledge of this phenomenon in the context of IIR as it has implications for test design of IIR studies. Design/methodology/approach – Order effect is studied via partly a literature review and partly an empirical IIR study. The empirical IIR study is designed as a classic between-groups design. The IIR search behaviour was logged and complementary post-search interviews were conducted. Findings – The order effect between groups and within search tasks were measured against nine classic IIR performance parameters of search interaction behaviour. Order effect is seen with respect to three performance parameters (website changes, visit of webpages, and formulation of queries) shown by an increase in activity on the last performed search. Further the theories with respect to motivation, fatigue, and the good-subject effect shed light on how and why order effect may affect test participants’ IR system interaction and search behaviour. Research limitations/implications – Insight about order effect has implications for test design of IIR studies and hence the knowledge base generated on the basis of such studies. Due to the limited sample of 20 test participants (Library and Information Science (LIS) students) inference statistics is not applicable; hence conclusions can be drawn from this sample of test participants only. Originality/value – Only few studies in LIS focus on order effect and none from the perspective of IIR.

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Online search behaviour of University of Zambia Library and Information Studies students

Library Hi Tech ◽

10.1108/lht-03-2017-0058 ◽

2018 ◽

Vol 36 (4) ◽

pp. 720-732

Author(s):

Francina N.S. Makondo ◽

Christine Wamunyima Kanyengo ◽

Fabian Kakana

Keyword(s):

Information Science ◽

Research Approach ◽

Web Searching ◽

Online Search ◽

Search Behaviour ◽

Content Type ◽

Topic Familiarity ◽

Subject Knowledge ◽

Web Experience ◽

The University

Purpose The purpose of this paper is to investigate factors that affect web searching behaviour of the students of the University of Zambia (UNZA). Design/methodology/approach This study adopted a qualitative research approach in order to get an insight into the interactions of the students at the UNZA with the real web situation. A post-search questionnaire was used as a tool to gather information from 65 Library and Information Science students about search techniques used, web experience, and subject knowledge of users. Findings This study shows that the main purpose for using the internet by students at the UNZA is for academic work. The findings also show that factors such as experience and topic familiarity had an effect on search behaviour, whereas, age of searcher did not affect the search technique used. Google was preferred for searching more than electronic databases. Originality/value This is the first systematic examination of students online search behaviour in Zambia. It allows the researchers to compare with search behaviour of students in a different social economic environment.

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Image needs on social Q&A sites: a comparison of Zhihu and Baidu Zhidao

The Electronic Library ◽

10.1108/el-09-2018-0192 ◽

2019 ◽

Vol 37 (3) ◽

pp. 454-473

Author(s):

Shengli Deng ◽

Anqi Zhao ◽

Ruhua Huang ◽

Haiping Zhao

Keyword(s):

Design Methodology ◽

Qualitative Content Analysis ◽

Image Search ◽

Search Behaviour ◽

Content Type ◽

Subjective Evaluations ◽

Retrieval Systems ◽

Subsequent Discussion ◽

The Difference ◽

Practical Implications

Purpose This study aims to examine why users search for images, how users describe their image needs and what the images are used for by analysing questions obtained from two Chinese social Q&A sites, Zhihu and Baidu Zhidao. Design/methodology/approach A total of 1,402 image questions were collected from Zhihu and Baidu Zhidao. Both quantitative analysis and qualitative content analysis were performed to identify user image needs and the potential differences on the two social Q&A sites. Findings Question-asker’s intention varies in different platforms. Zhihu users asked questions mainly aiming at a promotion of subsequent discussion, whereas users of Baidu Zhidao often did so to seek information. Syntactic attributes were not frequently used in both two sites. Zhihu users were more likely to express subjective evaluations on images (concept, emotion, theme and style) in their questions than users of Baidu Zhidao. In contrast, questions from Baidu Zhidao showed a tendency to more frequently include descriptive metadata (rights, format, size, quality and authenticity) and semantic attributes (generic activity, specific people, fashion and text) of the images than questions from Zhihu. Learning was an important use on social Q&A sites, especially on Baidu Zhidao. In addition, the images were primarily used to trigger emotion or served a persuasive purpose in Zhihu. Practical implications This study contributes to a better understanding of user image search behaviour, and the findings could be used to develop better image services on social Q&A sites. Meanwhile, the image attributes extracted from the questions are conducive to the improvement of image retrieval systems. Originality/value This study explored the features of image needs on social Q&A sites, especially considering image use specified in the question. The difference of image needs between two Chinese social Q&A sites (Zhihu and Baidu Zhidao) was identified.

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Low-Cost Image Search System on Off-Line Situation

Electronics ◽

10.3390/electronics9010153 ◽

2020 ◽

Vol 9 (1) ◽

pp. 153 ◽

Cited By ~ 1

Author(s):

Mery Diana ◽

Juntaro Chikama ◽

Motoki Amagasaki ◽

Masahiro Iida

Keyword(s):

Low Cost ◽

Model Performance ◽

Cosine Similarity ◽

Raspberry Pi ◽

Image Search ◽

Search Tasks ◽

Cost System ◽

Network Properties ◽

General Average ◽

Fully Connected

Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we use the general average pooling layer to replace the fully connected layers on the convolutional neural network (CNN) model, used in the previous study, to reduce the number of network properties without decreasing the model performance in developing image classification for image search tasks. We apply the cosine similarity to measure the characteristic similarity between the feature vector of image input and extracting feature vectors from testing images in the database. The result of the cosine similarity calculation will show the image as the result of the searching image task. In the implementation, we use Raspberry Pi 3 as a low-cost hardware and CIFAR-10 dataset for training and testing images. Base on the development and implementation, the accuracy of the model is 68%, and the system generates the result of the image search base on the characteristic similarity of the images.

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