%0 Conference Paper %1 Papadakis:2012:CSR:2254129.2254149 %A Papadakis, George %A Kawase, Ricardo %A Herder, Eelco %B Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics %C New York, NY, USA %D 2012 %I ACM %K myown %P 14:1--14:12 %R 10.1145/2254129.2254149 %T Client- and server-side revisitation prediction with SUPRA %U http://doi.acm.org/10.1145/2254129.2254149 %X Users of collaborative applications as well as individual users in their private environment return to previously visited Web pages for various reasons; apart from pages visited due to backtracking, they typically have a number of favorite or important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce a library of methods that facilitate revisitation through the effective prediction of the next page request. It is based on a generic framework that inherently incorporates contextual information, handling uniformly both server- and the client-side applications. Unlike other existing approaches, the methods it encompasses are real-time, since they do not rely on training data or machine learning algorithms. We evaluate them over two large, real-world datasets, with the outcomes suggesting a significant improvement over methods typically used in this context. We have also made our implementation and data publicly available, thus encouraging other researchers to use it as a benchmark and to extend it with new techniques for supporting user's navigational activity. %@ 978-1-4503-0915-8

@inproceedings{Papadakis:2012:CSR:2254129.2254149, abstract = {Users of collaborative applications as well as individual users in their private environment return to previously visited Web pages for various reasons; apart from pages visited due to backtracking, they typically have a number of favorite or important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce a library of methods that facilitate revisitation through the effective prediction of the next page request. It is based on a generic framework that inherently incorporates contextual information, handling uniformly both server- and the client-side applications. Unlike other existing approaches, the methods it encompasses are real-time, since they do not rely on training data or machine learning algorithms. We evaluate them over two large, real-world datasets, with the outcomes suggesting a significant improvement over methods typically used in this context. We have also made our implementation and data publicly available, thus encouraging other researchers to use it as a benchmark and to extend it with new techniques for supporting user's navigational activity.}, acmid = {2254149}, added-at = {2013-01-22T15:19:24.000+0100}, address = {New York, NY, USA}, articleno = {14}, author = {Papadakis, George and Kawase, Ricardo and Herder, Eelco}, biburl = {https://www.bibsonomy.org/bibtex/2921c514c2c03b864cb15609d94295a4c/kawase}, booktitle = {Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics}, description = {Client- and server-side revisitation prediction with SUPRA}, doi = {10.1145/2254129.2254149}, interhash = {c7fd5821ae7e1fd146683adf3fa83162}, intrahash = {921c514c2c03b864cb15609d94295a4c}, isbn = {978-1-4503-0915-8}, keywords = {myown}, location = {Craiova, Romania}, numpages = {12}, pages = {14:1--14:12}, publisher = {ACM}, series = {WIMS '12}, timestamp = {2013-02-15T11:33:54.000+0100}, title = {Client- and server-side revisitation prediction with SUPRA}, url = {http://doi.acm.org/10.1145/2254129.2254149}, year = 2012 }