Incremental C-Rank: An effective and efficient ranking algorithm for dynamic Web environments
- Authors
- Koo, Jangwan; Chae, Dong-Kyu; Kim, Dong-Jin; Kim, Sang-Wook
- Issue Date
- Jul-2019
- Publisher
- ELSEVIER
- Keywords
- Information retrieval; Ranking algorithm; Dynamic ranking
- Citation
- KNOWLEDGE-BASED SYSTEMS, v.176, pp.147 - 158
- Indexed
- SCIE
SCOPUS
- Journal Title
- KNOWLEDGE-BASED SYSTEMS
- Volume
- 176
- Start Page
- 147
- End Page
- 158
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147495
- DOI
- 10.1016/j.knosys.2019.03.034
- ISSN
- 0950-7051
- Abstract
- Web page ranking is one of the core components of search engines. Given a user query, ranking aims to provide a ranked list of Web pages that the user is likely to prefer the most. By and large, the ranking algorithms can be categorized into content-based approaches, link-based approaches, and hybrid approaches. Hybrid ranking algorithms, which exploit both the content and link information, are the most popular and extensively studied techniques. Among the hybrid algorithms, C-Rank combines content and link information in a very effective way using the concept of contribution. This algorithm is known to provide high performance in terms of both accurate and prompt responses to user queries. However, C-Rank suffers from very high costs to reflect the highly dynamic and extremely frequent changes in the World Wide Web, because it re-computes all of the C-Rank scores used for ranking from scratch to reflect the changes. As a result, C-Rank may be considered inappropriate to provide users with accurate and up-to-date search results. This paper aims to remedy this limitation of C-Rank. We propose incremental C-Rank, which is designed to update the C-Rank scores of only a carefully chosen portion of the Web pages rather than those of all of the Web pages without any accuracy loss. Our experimental results on a real-world dataset confirm both the effectiveness and efficiency of our proposed method.
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