Causal Dependencies for Future Interest Prediction on Twitter

Author:

Arabzadeh Negar1,Fani Hossein2,Zarrinkalam Fattane1,Navivala Ahmed1,Bagheri Ebrahim1

Affiliation:

1. Ryerson University, Toronto, ON, Canada

2. University of New Brunswick, Fredericton, NB, Canada

Funder

Natural Sciences and Engineering Research Council Canada

Publisher

ACM

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. CausalMMM: Learning Causal Structure for Marketing Mix Modeling;Proceedings of the 17th ACM International Conference on Web Search and Data Mining;2024-03-04

2. A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecasting;Neural Computing and Applications;2023-10-04

3. Exploring the Utility of Social Content for Understanding Future In-Demand Skills;Proceedings of the ACM on Human-Computer Interaction;2022-11-07

4. BERT and Word Embedding for Interest Mining of Instagram Users;Advances in Computational Collective Intelligence;2022

5. On the Congruence Between Online Social Content and Future IT Skill Demand;Proceedings of the ACM on Human-Computer Interaction;2021-10-13

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