Information scent as a driver of Web behavior graphs

2001 ◽  
Author(s):  
Stuart K. Card ◽  
Peter Pirolli ◽  
Mija Van Der Wege ◽  
Julie B. Morrison ◽  
Robert W. Reeder ◽  
...  
Keyword(s):  
Information Scent

Web Design Based on User Browsing Patterns

2011 ◽  
pp. 2074-2079
Author(s):  
Yinghui Yang
Keyword(s):  
Web Sites ◽  
Web Site ◽  
Web Design ◽  
Online Information ◽  
Navigation Data ◽  
Information Scent ◽  
Site Search ◽  
User Expectations ◽  
Corporate Web Sites ◽  
Search For Information

It is hard to organize a website such that pages are located where users expect to find them. Consider a visitor to an e-Commerce website in searching for a scanner. There are two ways he could find information he is looking for. One is to use the search function provided by the website. The other one is to follow the links on the website. This chapter focuses on the second case. Will he click on the link “Electronics” or “Computers” to find the scanner? For the website designer, should the scanner page be put under Electronics, Computers or both? This problem occurs across all kinds of websites, including B2C shops, B2B marketplaces, corporate web-sites and content websites. Through web usages mining, we can automatically discover pages in a website whose location is different from where users expect to find them. This problem of matching website organization with user expectations is pervasive across most websites. Since web users are heterogeneous, the question is essentially how to design a website so that majority of the users find it easy to navigate. Here, we focus on the problem of browsing within a single domain/web site (search engines are not involved since it’s a totally different way of finding information on a web site.) There are numerous reasons why users fail to find the information they are looking for when browse on a web site. Here in this chapter, we focus on the following reason. Users follow links when browsing online. Information scent guides them to select certain links to follow in search for information. If the content is not located where the users expect it to be, the users will fail to find it. How we analyze web navigation data to identify such user browsing patterns and use them to improve web design is an important task.

The effects of information scent on visual search in the hyperbolic tree browser

2003 ◽  
Vol 10 (1) ◽  
pp. 20-53 ◽  
Author(s):  
Peter Pirolli ◽  
Stuart K. Card ◽  
Mija M. Van Der Wege
Keyword(s):  
Visual Search ◽  
Information Scent

scholarly journals Visualizing Automatically Detected Periodic Network Activity

2018 ◽  
Author(s):  
Robert Gove ◽  
Lauren Deason
Keyword(s):  
Fourier Transforms ◽  
Network Activity ◽  
Periodic Signal ◽  
Periodic Activity ◽  
Large Organization ◽  
Discrete Fourier Transforms ◽  
Information Scent ◽  
Software Updates ◽  
Periodic Signals ◽  
Distinct Period

Malware frequently leaves periodic signals in network logs, but these signals are easily drowned out by non-malicious periodic network activity, such as software updates and other polling activity. This paper describes a novel algorithm based on Discrete Fourier Transforms capable of detecting multiple distinct period lengths in a given timeseries. We pair the output of this algorithm with aggregation summary tables that give users information scent about which detections are worth investigating based on the metadata of the log events rather than the periodic signal. A visualization of selected detections enables users to see all detected period lengths per entity, and compare detections between entities to check for coordinated activity. We evaluate our approach on real-world netflow and DNS data from a large organization, demonstrating how to successfully find malicious periodic activity in a large pool of noise and non-malicious periodic activity.

Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search

2018 ◽  
pp. 266-291
Author(s):  
Suruchi Chawla
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Multi Agent System ◽  
Information Need ◽  
Agent System ◽  
Query Log ◽  
Information Scent ◽  
Multi Agent ◽  
Log Mining ◽  
Query Log Mining

This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recommendations to users who search with similar intents. This multi-agent system based on clustered query sessions uses query operations like expansion and recommendation to infer the information need of user search queries and recommends Hubs and authorities for effective web search.

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