Name
Abstract | ||
|---|---|---|
We introduce a novel approach to identifying Web search user sessions based on the burstiness of users' activity. Our method is user-centered rather than population-centered or system-centered and can be deployed in situations in which users choose to withhold personal content information. We adopt a hierarchical agglomerative clustering approach with a stopping criterion that is statistically motivated by users' activities. An evaluation based on extracts from AOL Search™ logs reveals that our algorithm achieves 98% accuracy in identifying session boundaries compared to human judgments. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1002/meet.14504301312 | ASIST |
Field | DocType | Volume |
Hierarchical clustering,Data mining,Computer science,Hierarchical clustering of networks,Burstiness,Artificial intelligence,Brown clustering,Machine learning | Conference | 43 |
Issue | Citations | PageRank |
1 | 23 | 0.86 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Craig Murray | 1 | 185 | 10.80 |
| Jimmy Lin | 2 | 4800 | 376.93 |
| Abdur Chowdhury | 3 | 2013 | 160.59 |