Incorporating the surfing behavior of web users into pagerank

Ashyralyyev, Shatlyk; Cambazoğlu, B. Barla; Aykanat, Cevdet

doi:10.1145/2505515.2505668

Cited by 3 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors evaluated ClickRank using a large volume of user browsing logs collected from the Yahoo! toolbar and demonstrated that this method outperforms both PageRank and BrowseRank; see also [16].…”

Section: Using Browsing Trails and Page Behavior For Primary Rankingmentioning

confidence: 88%

Social Search

Brusilovsky

Smyth

Shapira

2018

Social Information Access

View full text Add to dashboard Cite

Today, most people find what they are looking for online by using search engines such as Google, Bing, or Baidu. Modern web search engines have evolved from their roots in information retrieval to developing new ways to cope with the unique nature of web search. In this chapter, we review recent research that aims to make search a more social activity by combining readily available social signals with various strategies for using these signals to influence or adapt more conventional search results. The chapter begins by framing the social search landscape in terms of the sources of data available and the ways in which this can be leveraged before, during, and after search. This includes a number of detailed case studies that serve to mark important milestones in the evolution of social search research and practice.

show abstract

Section: Using Browsing Trails and Page Behavior For Primary Rankingmentioning

confidence: 88%

Social Search

Brusilovsky

Smyth

Shapira

2018

Social Information Access

View full text Add to dashboard Cite

show abstract

“…The authors of [250], [251] studied the understanding and utilization of the browsing behaviors of network users and methods to improve their browsing experiences. In [252], the PageRank algorithm was improved based on web user browsing data. The authors of [253] studied user behavior analysis based on focus window transition logs; [254] proposed a network user clustering analysis; [255] performed a Markov study of user network behavior; [256] studied the intention and randomness of users' web browsing behaviors; [257] studied the degradation of network users' comment behavior; and [258] studied browsing data collection and privacy protection for network users.…”

Section: ) User Web Browsing Behavior Analysismentioning

confidence: 99%

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

et al. 2019

View full text Add to dashboard Cite

With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.

show abstract

Unsupervised Domain Ranking in Large-Scale Web Crawls

Cui¹,

Sparkman

Lee

et al. 2018

ACM Trans. Web

View full text Add to dashboard Cite

With the proliferation of web spam and infinite autogenerated web content, large-scale web crawlers require low-complexity ranking methods to effectively budget their limited resources and allocate bandwidth to reputable sites. In this work, we assume crawls that produce frontiers orders of magnitude larger than RAM, where sorting of pending URLs is infeasible in real time. Under these constraints, the main objective is to quickly compute domain budgets and decide which of them can be massively crawled. Those ranked at the top of the list receive aggressive crawling allowances, while all other domains are visited at some small default rate. To shed light on Internet-wide spam avoidance, we study topology-based ranking algorithms on domain-level graphs from the two largest academic crawls: a 6.3B-page IRLbot dataset and a 1B-page ClueWeb09 exploration. We first propose a new methodology for comparing the various rankings and then show that in-degree BFS-based techniques decisively outperform classic PageRank-style methods, including TrustRank. However, since BFS requires several orders of magnitude higher overhead and is generally infeasible for real-time use, we propose a fast, accurate, and scalable estimation method called TSE that can achieve much better crawl prioritization in practice. It is especially beneficial in applications with limited hardware resources.

show abstract

Incorporating the surfing behavior of web users into pagerank

Cited by 3 publications

References 49 publications

Social Search

Social Search

Behavior Analysis for Electronic Commerce Trading Systems: A Survey

Unsupervised Domain Ranking in Large-Scale Web Crawls

Contact Info

Product

Resources

About