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| Content Provider | ACM Digital Library |
|---|---|
| Author | Tseng, Belle L. Song, Xiaodan Chi, Yun Hino, Koji |
| Abstract | Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction . identify where information should flow to, and (2) ranking . identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow. |
| Starting Page | 191 |
| Ending Page | 200 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781595936547 |
| DOI | 10.1145/1242572.1242599 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2007-05-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Social influence Collaborative filtering Continuous-time markov chain Information flow Diffusion of innovation Web ranking Recommendation |
| Content Type | Text |
| Resource Type | Article |
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