Name
Abstract | ||
|---|---|---|
This paper proposes a learning model that tries to infer a user's topical expertise using multiple types of user-related data from Twitter such as tweets posted by the user and the characteristics of their followers. It considers inference consistency of different types of user data in the process of learning and aims to deliver accurate and effective inference results, even in cases where some types of data are missing for a user, e.g. the user has yet to post any tweets. Experiments conducted on a large-scale Twitter dataset show that our model outperforms several baseline approaches which use only a single type of user data for inference. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3079628.3079646 | UMAP |
Field | DocType | Citations |
World Wide Web,Social network,Social media,Computer science,Inference,Data type,User modeling,Personalization | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Xu | 1 | 7 | 2.13 |
| Dong Zhou | 2 | 342 | 25.99 |
| Séamus Lawless | 3 | 111 | 30.18 |