Simultaneous Inference of User Representations and Trust
This work addresses the need for credible information in social media applications by improving trust prediction, but it is incremental as it builds on existing representation learning methods.
The paper tackles the problem of predicting trust relations between social media users, which is challenging due to scarce and skewed data, by proposing a representation learning approach that simultaneously learns user embeddings and a trust prediction model, achieving a high F-score of 92.65% on a dataset of approximately 356K user pairs.
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 $\sim$356K user pairs show that the proposed method can obtain an high F-score of 92.65%.