JOURNAL ARTICLE

Implicit relevance feedback for context-aware information retrieval in UbiLearning environments

Abstract

Ubiquitous Learning (UbiLearning) environments heavily employ mobile devices to empower users with mobility and tooling support to learn anytime, anywhere. Introducing mobile devices in educational settings imposes constraints on search behavior due to limited resources on these devices such as small screens and restricted input functionalities. Targeting this scenario, this paper proposes an architecture for implicit relevance feedback which considers users' work context to expand search queries in order to satisfy information needs with greater precision. Evaluation results conducted over a local test collection enriched with contextual features reveal improvements on average precision when compared to a pseudo relevance feedback baseline.

Keywords:
Relevance (law) Computer science Relevance feedback Context (archaeology) Mobile device Human–computer interaction Baseline (sea) Information retrieval Data science Multimedia World Wide Web Artificial intelligence Image retrieval

Metrics

4
Cited By
1.24
FWCI (Field Weighted Citation Impact)
16
Refs
0.84
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Context-Aware Activity Recognition Systems
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
ICT in Developing Communities
Physical Sciences →  Computer Science →  Information Systems
Mobile Crowdsensing and Crowdsourcing
Physical Sciences →  Computer Science →  Computer Science Applications

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