Work

End-to-end vertical web search pseudo relevance feedback queries recommendation software

Year: 2024

Type: software-paper

Abstract: Users' web information needs are increasingly exploratory, seeking to navigate unfamiliar domains and discover knowledge. However, existing search engines struggle with ambiguous queries, leading to irrelevant results. To address this, we propose an architecture that autonomously extracts domain knowledge from initial queries. This system transforms queries into a semantic model, suggesting relevant queries to aid exploration. Evaluated against Google, our architecture achieved 89 % accuracy in automated query recommendation, with 85.4 % system usability and an "A" ranking. This approach enhances exploratory search by facilitating clearer, more effective query formulation, thus improving information retrieval for users.

Source: SoftwareX

DOI: 10.1016/j.softx.2024.101872

Authors: Tajmir Khan, Umer Rashid, Abdur Rehman Khan, Naveed Ahmad, Mohammed Ali Alshara

Institutions: Quaid-i-Azam University, Queensland University of Technology, Prince Sultan University

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $970

Funder: Prince Sultan University

OpenAlex ID: W4402289385

Cites: 26

Cited by: 2

Related to: 10