Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2018-07-19Online Publication Date
2018-07-19Print Publication Date
2018Permanent link to this record
http://hdl.handle.net/10754/628781
Metadata
Show full item recordAbstract
In this paper, we study the problem of dynamic user profiling in Twitter. We address the problem by proposing a dynamic user and word embedding model (DUWE), a scalable black-box variational inference algorithm, and a streaming keyword diversification model (SKDM). DUWE dynamically tracks the semantic representations of users and words over time and models their embeddings in the same space so that their similarities can be effectively measured. Our inference algorithm works with a convex objective function that ensures the robustness of the learnt embeddings. SKDM aims at retrieving top-K relevant and diversified keywords to profile users' dynamic interests. Experiments on a Twitter dataset demonstrate that our proposed embedding algorithms outperform state-of-the-art non-dynamic and dynamic embedding and topic models.Citation
Liang S, Zhang X, Ren Z, Kanoulas E (2018) Dynamic Embeddings for User Profiling in Twitter. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining - KDD ’18. Available: http://dx.doi.org/10.1145/3219819.3220043.Sponsors
This work was supported by the King Abdullah University of Science and Technology (KAUST), Saudi Arabia.Conference/Event name
24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018Additional Links
https://dl.acm.org/citation.cfm?doid=3219819.3220043ae974a485f413a2113503eed53cd6c53
10.1145/3219819.3220043