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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Controversial users demand local trust metrics: an experimental study on Epinions.com community
Paolo Massa,Paolo Avesani +1 more
TL;DR: It is argued, using computational experiments, that the existence of controversial users demands Local Trust Metrics, techniques able to predict the trustworthiness of an user in a personalized way, depending on the very personal view of the judging user.
Yahoo! Learning to Rank Challenge Overview
Olivier Chapelle,Yi Chang +1 more
TL;DR: The Yahoo! Learning to Rank Challenge as discussed by the authors was organized to promote the development of state-of-the-art learning-to-rank algorithms for web search ranking, which has gained a lot of interest in the recent years.
Book
Community Detection and Mining in Social Media
TL;DR: This book discusses graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media, and demonstrates how discovered patterns of communities can be used for social media mining.
Journal ArticleDOI
Web Crawling
Christopher Olston,Marc Najork +1 more
TL;DR: The fundamental challenges of web crawling are outlined and the state-of-the-art models and solutions are described, and avenues for future work are highlighted.
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A Very Brief Introduction to Machine Learning With Applications to Communication Systems
TL;DR: In this paper, the authors provide a high-level introduction to the basics of supervised and unsupervised learning, exemplifying applications to communication networks by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack, with an emphasis on the physical layer.
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.