Details
| Original language | English |
|---|---|
| Title of host publication | Web Engineering |
| Subtitle of host publication | 11th International Conference, ICWE 2011, Proceedings |
| Pages | 258-273 |
| Number of pages | 16 |
| Publication status | Published - 2011 |
| Event | 11th International Conference on Web Engineering, ICWE 2011 - Paphos, Cyprus Duration: 20 Jun 2011 → 24 Jun 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6757 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Web browser users return to Web pages for various reasons. Apart from pages visited due to backtracking, they typically have a number of favorite/important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce the architecture of a system that facilitates revisitations through the effective prediction of the next page request. It consists of three layers, each dealing with a specific aspect of revisitation patterns: the first one estimates the value of each page by balancing the recency and the frequency of its requests; the second one captures the contextual regularities in users' navigational activity in order to promote related pages, and the third one dynamically adapts the page associations of the second layer to the constant drift in the interests of users. For each layer, we introduce several methods, and evaluate them over a large, real-world dataset. The outcomes of our experimental evaluation suggest a significant improvement over other methods typically used in this context.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Web Engineering : 11th International Conference, ICWE 2011, Proceedings. 2011. p. 258-273 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6757 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A layered approach to revisitation prediction
AU - Papadakis, George
AU - Kawase, Ricardo
AU - Herder, Eelco
AU - Niederée, Claudia
N1 - Funding Information: This research has been co-funded by the European Commission within the eCon-tentplus targeted project OpenScout, grant ECP 2008 EDU 428016.
PY - 2011
Y1 - 2011
N2 - Web browser users return to Web pages for various reasons. Apart from pages visited due to backtracking, they typically have a number of favorite/important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce the architecture of a system that facilitates revisitations through the effective prediction of the next page request. It consists of three layers, each dealing with a specific aspect of revisitation patterns: the first one estimates the value of each page by balancing the recency and the frequency of its requests; the second one captures the contextual regularities in users' navigational activity in order to promote related pages, and the third one dynamically adapts the page associations of the second layer to the constant drift in the interests of users. For each layer, we introduce several methods, and evaluate them over a large, real-world dataset. The outcomes of our experimental evaluation suggest a significant improvement over other methods typically used in this context.
AB - Web browser users return to Web pages for various reasons. Apart from pages visited due to backtracking, they typically have a number of favorite/important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce the architecture of a system that facilitates revisitations through the effective prediction of the next page request. It consists of three layers, each dealing with a specific aspect of revisitation patterns: the first one estimates the value of each page by balancing the recency and the frequency of its requests; the second one captures the contextual regularities in users' navigational activity in order to promote related pages, and the third one dynamically adapts the page associations of the second layer to the constant drift in the interests of users. For each layer, we introduce several methods, and evaluate them over a large, real-world dataset. The outcomes of our experimental evaluation suggest a significant improvement over other methods typically used in this context.
UR - http://www.scopus.com/inward/record.url?scp=79960231789&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22233-7_18
DO - 10.1007/978-3-642-22233-7_18
M3 - Conference contribution
AN - SCOPUS:79960231789
SN - 9783642222320
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 258
EP - 273
BT - Web Engineering
T2 - 11th International Conference on Web Engineering, ICWE 2011
Y2 - 20 June 2011 through 24 June 2011
ER -