A Comprehensive Survey of Facet Ranking Approaches Used in Faceted Search Systems

Author:

Ali Esraa1ORCID,Caputo Annalina1,Jones Gareth J. F.1ORCID

Affiliation:

1. ADAPT Centre, School of Computing, Dublin City University, D09 E432 Dublin, Ireland

Abstract

Faceted Search Systems (FSSs) have gained prominence as one of the dominant search approaches in vertical search systems. They provide facets to educate users about the information space and allow them to refine their search query and navigate back and forth between resources on a single results page. Despite the importance of this problem, it is rare to find studies dedicated solely to the investigation of facet ranking methods, nor to how this step, aside from other aspects of faceted search, affects the user’s search experience. The objective of this survey paper is to review the state of the art in research related to faceted search systems, with a focus on existing facet ranking approaches and the key challenges posed by this problem. In addition to that, this survey also investigates state-of-the-art FSS evaluation frameworks and the most commonly used techniques and metrics to evaluate facet ranking approaches. It also lays out criteria for dataset appropriateness and its needed structure to be used in evaluating facet ranking methods aside from other FSS aspects. This paper concludes by highlighting gaps in the current research and future research directions related to this area.

Funder

Science Foundation Ireland Research Centres

European Regional Development Fund

Publisher

MDPI AG

Subject

Information Systems

Reference71 articles.

1. Oren, E., Delbru, R., and Decker, S. (2006). International Semantic Web Conference, Springer.

2. Niu, X., Fan, X., and Zhang, T. (2019). Understanding Faceted Search from Data Science and Human Factor Perspectives. ACM Trans. Inf. Syst., 37.

3. Koren, J., Zhang, Y., and Liu, X. (2008, January 21–25). Personalized interactive faceted search. Proceedings of the 17th international conference on World Wide Web, Beijing China.

4. Ben-Yitzhak, O., Golbandi, N., Har’El, N., Lempel, R., Neumann, A., Ofek-Koifman, S., Sheinwald, D., Shekita, E., Sznajder, B., and Yogev, S. (2008). Proceedings of the 2008 International Conference on Web Search and Data Mining, Palo Alto, CA, USA, 11–12 February 2008, Association for Computing Machinery.

5. Yee, K.P., Swearingen, K., Li, K., and Hearst, M. (2003, January 5–10). Faceted metadata for image search and browsing. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ft. Lauderdale, FL, USA.

Cited by 1 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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3