Name
Title | ||
|---|---|---|
Building a topic hierarchy from a data stream for conceptualizing user interest profiles |
Abstract | ||
|---|---|---|
Recent information technologies have produced a lot of user- participating community services, or Social Network Services (SNSs), and have received much attention as a pipeline of user behavioral information. This paper explains the conceptualization of a user interest profile (UIP) through a topic hierarchy, which learns topics and keywords from a domain data stream. The conceptualization enriches a UIP, consisting of user interests modeled as terms and term-weight, by providing contextual information of the UIP. For this, the topic hierarchy extracts topic-deterministic keywords and their semantic associations with domain topics, and thus the user keywords are converted into domain topics representing the context of user behaviors. This paper describes an empirical method to build a news topic hierarchy and its use for conceptualizing a series of keywords appearing in user SNS messages. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICTC.2012.6386832 | ICTC |
Keywords | Field | DocType |
data mining,learning (artificial intelligence),social networking (online),text analysis,contextual information,domain data stream,domain topic,information technology,keyword learning,news topic hierarchy,semantic association,social network services,term-weight,topic hierarchy building,topic learning,topic-deterministic keyword extraction,user sns message,user behavioral information,user interest profile conceptualization,user keyword,user-participating community service,conceptualization,topic extraction,topic hierarchy,user interest profile,semantics,vectors,learning artificial intelligence | World Wide Web,Contextual information,Social network,Information retrieval,Data stream,Information technology,Computer science,Conceptualization,Hierarchy,Semantics | Conference |
ISBN | Citations | PageRank |
978-1-4673-4827-0 | 1 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| hyun namgoong | 1 | 1 | 0.34 |
| kang yong lee | 2 | 1 | 0.34 |
| Kee-Seong Cho | 3 | 35 | 7.74 |
| jinuk jung | 4 | 1 | 0.34 |
| Hong-Gee Kim | 5 | 104 | 18.80 |