Details
| Original language | English |
|---|---|
| Pages (from-to) | 331-369 |
| Number of pages | 39 |
| Journal | User Modeling and User-Adapted Interaction |
| Volume | 25 |
| Issue number | 4 |
| Publication status | Published - 13 May 2015 |
Abstract
More than 45 % of the pages that we visit on the Web are pages that we have visited before. Browsers support revisits with various tools, including bookmarks, history views and URL auto-completion. However, these tools only support revisits to a small number of frequently and recently visited pages. Several browser plugins and extensions have been proposed to better support the long tail of less frequently visited pages, using recommendation and prediction techniques. In this article, we present a systematic overview of revisitation prediction techniques, distinguishing them into two main types and several subtypes. We also explain how the individual prediction techniques can be combined into comprehensive revisitation workflows that achieve higher accuracy. We investigate the performance of the most important workflows and provide a statistical analysis of the factors that affect their predictive accuracy. Further, we provide an upper bound for the accuracy of revisitation prediction using an ‘oracle’ that discards non-revisited pages.
Keywords
- Navigation entropy, Revisitation evaluation, Revisitation prediction, Web behavior
ASJC Scopus subject areas
- Social Sciences(all)
- Education
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Science Applications
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In: User Modeling and User-Adapted Interaction, Vol. 25, No. 4, 13.05.2015, p. 331-369.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Methods for web revisitation prediction
T2 - survey and experimentation
AU - Papadakis, George
AU - Kawase, Ricardo
AU - Herder, Eelco
AU - Nejdl, Wolfgang
PY - 2015/5/13
Y1 - 2015/5/13
N2 - More than 45 % of the pages that we visit on the Web are pages that we have visited before. Browsers support revisits with various tools, including bookmarks, history views and URL auto-completion. However, these tools only support revisits to a small number of frequently and recently visited pages. Several browser plugins and extensions have been proposed to better support the long tail of less frequently visited pages, using recommendation and prediction techniques. In this article, we present a systematic overview of revisitation prediction techniques, distinguishing them into two main types and several subtypes. We also explain how the individual prediction techniques can be combined into comprehensive revisitation workflows that achieve higher accuracy. We investigate the performance of the most important workflows and provide a statistical analysis of the factors that affect their predictive accuracy. Further, we provide an upper bound for the accuracy of revisitation prediction using an ‘oracle’ that discards non-revisited pages.
AB - More than 45 % of the pages that we visit on the Web are pages that we have visited before. Browsers support revisits with various tools, including bookmarks, history views and URL auto-completion. However, these tools only support revisits to a small number of frequently and recently visited pages. Several browser plugins and extensions have been proposed to better support the long tail of less frequently visited pages, using recommendation and prediction techniques. In this article, we present a systematic overview of revisitation prediction techniques, distinguishing them into two main types and several subtypes. We also explain how the individual prediction techniques can be combined into comprehensive revisitation workflows that achieve higher accuracy. We investigate the performance of the most important workflows and provide a statistical analysis of the factors that affect their predictive accuracy. Further, we provide an upper bound for the accuracy of revisitation prediction using an ‘oracle’ that discards non-revisited pages.
KW - Navigation entropy
KW - Revisitation evaluation
KW - Revisitation prediction
KW - Web behavior
UR - http://www.scopus.com/inward/record.url?scp=84942368307&partnerID=8YFLogxK
U2 - 10.1007/s11257-015-9161-7
DO - 10.1007/s11257-015-9161-7
M3 - Article
AN - SCOPUS:84942368307
VL - 25
SP - 331
EP - 369
JO - User Modeling and User-Adapted Interaction
JF - User Modeling and User-Adapted Interaction
SN - 0924-1868
IS - 4
ER -