TY - GEN
T1 - Ranking Web news via homepage visual layout and cross-site voting
AU - Yao, Jinyi
AU - Wang, Jue
AU - Li, Zhiwei
AU - Li, Mingjing
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - Reading news is one of the most popular activities when people surf the internet. As too many news sources provide independent news information und each has its own preference, detecting unbiased important news might be very useful for users to keep up to date with what are happening in the world. In this paper we present a novel method to identify important news in web environment which consists of diversified online news sites. We observe that a piece of important news generally occupies visually significant place in some homepage of a news site and import news event will be reported by many news sites. To explore these two properties, we model the relationship between homepages, news and latent events by a tripartite graph, and present an algorithm to identify important news in this model. Based on this algorithm, we implement a system TOPSTORY to dynamically generate homepages for users to browse important news reports. Our experimental study indicates the effectiveness of proposed approach. © Springer-Verlag Berlin Heidelberg 2006.
AB - Reading news is one of the most popular activities when people surf the internet. As too many news sources provide independent news information und each has its own preference, detecting unbiased important news might be very useful for users to keep up to date with what are happening in the world. In this paper we present a novel method to identify important news in web environment which consists of diversified online news sites. We observe that a piece of important news generally occupies visually significant place in some homepage of a news site and import news event will be reported by many news sites. To explore these two properties, we model the relationship between homepages, news and latent events by a tripartite graph, and present an algorithm to identify important news in this model. Based on this algorithm, we implement a system TOPSTORY to dynamically generate homepages for users to browse important news reports. Our experimental study indicates the effectiveness of proposed approach. © Springer-Verlag Berlin Heidelberg 2006.
UR - https://www.scopus.com/pages/publications/33745837747
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33745837747&origin=recordpage
U2 - 10.1007/11735106_13
DO - 10.1007/11735106_13
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540333479
SN - 9783540333470
VL - 3936 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 142
BT - Advances in Information Retrieval - 28th European Conference on IR Research, ECIR 2006, Proceedings
PB - Springer Verlag
T2 - 28th European Conference on Information Retrieval Research, ECIR 2006
Y2 - 10 April 2006 through 12 April 2006
ER -