Open AccessProceedings Article
The PageRank Citation Ranking : Bringing Order to the Web
Lawrence Page,Sergey Brin,Rajeev Motwani,Terry Winograd +3 more
- Vol. 98, pp 161-172
TLDR
This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.Abstract:
The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.read more
Citations
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Book ChapterDOI
Using PageRank to Characterize Web Structure
TL;DR: This work studies the distribution of PageRank values (used in the Google search engine) on the Web, and develops detailed models for the Web graph that explain this observation, and remain faithful to previously studied degree distributions.
Journal ArticleDOI
Business Intelligence and Analytics: Research Directions
TL;DR: The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications to categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics.
MonographDOI
Mathematics for Machine Learning
TL;DR: This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites to derive four central machine learning methods.
Recognizing Nepotistic Links on the Web
TL;DR: High accuracy in initial experiments is reported to show the potential for using a machine learning tool to automatically recognize and eliminate nepotistic links— links between pages that are present for reasons other than merit.
Journal ArticleDOI
Graph-based term weighting for information retrieval
Roi Blanco,Christina Lioma +1 more
TL;DR: This work proposes a principled graph-theoretic approach of computing term weights and integrating discourse aspects into retrieval, and experimentally shows that this type of ranking performs comparably to BM25, and can even outperform it, across different TREC datasets and evaluation measures.
References
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Journal Article
The Anatomy of a Large-Scale Hypertextual Web Search Engine.
Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
Journal ArticleDOI
Efficient crawling through URL ordering
TL;DR: In this paper, the authors study in what order a crawler should visit the URLs it has seen, in order to obtain more "important" pages first, and they show that a good ordering scheme can obtain important pages significantly faster than one without.
Proceedings ArticleDOI
Silk from a sow's ear: extracting usable structures from the Web
TL;DR: This paper presents the exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size.
Proceedings ArticleDOI
HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
TL;DR: Experience with HyPursuit suggests that abstraction functions based on hypertext clustering can be used to construct meaningful and scalable cluster hierarchies, and is encouraged by preliminary results on clustering based on both document contents and hyperlink structures.
Journal ArticleDOI
The quest for correct information on the Web: hyper search engines
TL;DR: This paper presents a novel method to extract from a web object its “hyper” informative content, in contrast with current search engines, which only deal with the “textual’ informative content.