User Reputation Evaluation Using Co-occurrence Feature and Collective Intelligence

Title
User Reputation Evaluation Using Co-occurrence Feature and Collective Intelligence
Authors
차정원이현우한요섭김래현
Keywords
PageRank; User Reputation; Co-occurrence; Collective Intelligence
Issue Date
2009-07
Publisher
Lecture notes in computer science
Citation
VOL 5621, 362-369
Abstract
It becomes more difficult to find valuable contents in the Web 2.0 environment since lots of inexperienced users provide many unorganized contents. In the previous researches, people has proved that non-text information such as the number of references, the number of supports, and the length of answers is effective to evaluate answers to a question in a online QnA service site. However, these features can be changed easily by users and cannot reflect social activity of users. In this paper, we propose a new method to evaluate user reputation using co-occurrence features between question and answers, and collective intelligence. If we are able to calculate user reputation, then we can estimate the worth of contents that has small number of reference and small number of support. We compute the user reputation using a modified PageRank algorithm. The experiment results show that our proposed method is effective and useful for identifying such contents.
URI
https://pubs.kist.re.kr/handle/201004/35614
ISSN
0302-9743
Appears in Collections:
KIST Publication > Article
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