Comprehensive Review and Future Research Directions on Dynamic Faceted Search

Author:

Mahdi Mohammed NajahORCID,Ahmad Abdul Rahim,Natiq HayderORCID,Subhi Mohammed Ahmed,Qassim Qais Saif

Abstract

In modern society, the increasing number of web search operations on various search engines has become ubiquitous due to the significant number of results presented to the users and the incompetent result-ranking mechanism in some domains, such as medical, law, and academia. As a result, the user is overwhelmed with a large number of misranked or uncategorized search results. One of the most promising technologies to reduce the number of results and provide desirable information to the users is dynamic faceted filters. Therefore, this paper extensively reviews related research articles published in IEEE Xplore, Web of Science, and the ACM digital library. As a result, a total of 170 related research papers were considered and organized into five categories. The main contribution of this paper is to provide a detailed analysis of the faceted search’s fundamental attributes, as well as to demonstrate the motivation from the usage, concerns, challenges, and recommendations to enhance the use of the faceted approach among web search service providers.

Funder

Universiti Tenaga Nasional (UNITEN), Innovation and Research Management Center

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Artificial Intelligence as a Tool for Human-Machine Partnership in the Educational Process;Lecture Notes in Networks and Systems;2024

2. Research Challenges and Future Facet Of Cellular Computing;2023 International Conference on Business Analytics for Technology and Security (ICBATS);2023-03-07

3. Technology, Computation and Artificial Intelligence to Improve the Web Ecosystem;Applied Sciences;2022-12-07

4. Relation-aware collaborative autoencoder for personalized multiple facet selection;Knowledge-Based Systems;2022-06

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