Exploring the context of visual information seeking
August 06, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shahram Sedghi, Zeinab Shourmeij, Iman Tahamtan
arXiv ID
1708.01856
Category
cs.IR: Information Retrieval
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Information seeking is an interactive behaviour of the end users with information systems, which occurs in a real environment known as context. Context affects information-seeking behaviour in many different ways. The purpose of this paper is to investigate the factors that potentially constitute the context of visual information seeking. We used a Straussian version of grounded theory, a qualitative approach, to conduct the study. Using a purposive sampling method, 28 subjects participated in the study. The data were analysed using open, axial and selective coding in MAXQDA software. The contextual factors influencing visual information seeking were classified into seven categories, including: user characteristics, general search features, visual search features, display of results, accessibility of results, task type and environmental factors. This study contributes to a better understanding of how people conduct searches in and interact with visual search interfaces. Results have important implications for the designers of information retrieval systems. This paper is among the pioneer studies investigating contextual factors influencing information seeking in visual information retrieval systems.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted