Name
Abstract | ||
|---|---|---|
Inferring trust relations between social media users is critical for a number of applications wherein users seek credible information. The fact that available trust relations are scarce and skewed makes trust prediction a challenging task. To the best of our knowledge, this is the first work on exploring representation learning for trust prediction. We propose an approach that uses only a small amount of binary user-user trust relations to simultaneously learn user embeddings and a model to predict trust between user pairs. We empirically demonstrate that for trust prediction, our approach outperforms classifier-based approaches which use state-of-the-art representation learning methods like DeepWalk and LINE as features. We also conduct experiments which use embeddings pre-trained with DeepWalk and LINE each as an input to our model, resulting in further performance improvement. Experiments with a dataset of ~356K user pairs show that the proposed method can obtain a high F-score of 92.65%.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3110025.3110093 | ASONAM '17: Advances in Social Networks Analysis and Mining 2017
Sydney
Australia
July, 2017 |
DocType | Volume | ISBN |
Conference | abs/1706.00923 | 978-1-4503-4993-2 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| shashank gupta | 1 | 60 | 11.35 |
| Pulkit Parikh | 2 | 1 | 2.05 |
| Manish Gupta | 3 | 1358 | 98.09 |
| Vasudeva Varma | 4 | 640 | 95.84 |