Retrieving and organizing web pages by “information unit”
Proceedings of the 10th international conference on World Wide Web
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic model for retrospective news event detection
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Event detection from evolution of click-through data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Chinese new word detection from query logs
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Investigating query bursts in a web search engine
Web Intelligence and Agent Systems
| Hi-index | 0.00 |
Although most of existing research usually detects events by analyzing the content or structural information of Web documents, a recent direction is to study the usage data. In this paper, we focus on detecting events from Web click-through data generated by Web search engines. We propose a novel approach which effectively detects events from click-through data based on robust subspace analysis. We first transform click-through data to the 2D polar space. Next, an algorithm based on Generalized Principal Component Analysis (GPCA) is used to estimate subspaces of transformed data such that each subspace contains query sessions of similar topics. Then, we prune uninteresting subspaces which do not contain query sessions corresponding to real events by considering both the semantic certainty and the temporal certainty of query sessions in each subspace. Finally, various events are detected from interesting subspaces by utilizing a nonparametric clustering technique. Compared with existing approaches, our experimental results based on real-life click-through data have shown that the proposed approach is more accurate in detecting real events and more effective in determining the number of events.