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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhu, Yanqin Ji, Qijin Shen, Haifeng Mao, Yuqing |
| Abstract | Incentives play a pivotal role in stimulating user-generated content (UGC), which is critical to the viability and success of today's social computing services. Non-financial social incentives are generally effective in boosting the quantity, but have limited effect on the quality. Conversely, financial incentives generally motivate better quality, but often complicate the efforts to attract quantity. In this paper, we propose knowledge barter-auctioning, a non-financial remunerative mechanism that is particularly effective in stimulating the quality of UGC yet without detriment to its quantity. This mechanism provides an optimal way for the knowledge vendor to choose the best barter partner in order to maximise their expected revenue, which is an extrinsic motivation for the triumph of quality as UGC of higher quality will enable the vendor to attract more bidders and consequently make a higher revenue through the barter auction. We have conducted a series of experiments using a real-world dataset to analyse the ramifications of UGC quality in knowledge bartering processes. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450328883 |
| DOI | 10.1145/2639968.2640068 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-08-04 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Incentive User-generated content Bartering Optimal auction |
| Content Type | Text |
| Resource Type | Article |
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