Causal discovery from temporal data: An overview and new perspectives

C Gong, C Zhang, D Yao, J Bi, W Li, YJ Xu - ACM Computing Surveys, 2023 - dl.acm.org
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

A novel deep transfer learning framework with adversarial domain adaptation: application to financial time-series forecasting

D Zhang, R Lin, T Wei, L Ling, J Huang - Neural Computing and …, 2023 - Springer
Financial market prediction is generally regarded as one of the most challenging tasks in
data mining. Recent deep learning models have achieved success in improving the …

Extracting, mining and predicting users' interests from social media

F Zarrinkalam, S Faralli, G Piao… - … and Trends® in …, 2020 - nowpublishers.com
The abundance of user generated content on social media provides the opportunity to build
models that are able to accurately and effectively extract, mine and predict users' interests …

CausalMMM: Learning Causal Structure for Marketing Mix Modeling

C Gong, D Yao, L Zhang, S Chen, W Li, Y Su… - Proceedings of the 17th …, 2024 - dl.acm.org
In online advertising, marketing mix modeling (MMM) is employed to predict the gross
merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget …

BERT and word embedding for interest mining of instagram users

S Hamdi, A Hamdi, S Ben Yahia - International Conference on …, 2022 - Springer
With more than one billion monthly active users and nearly 100 million photos shared on the
platform daily, Instagram has become among the richest sources of information for detecting …

On the Congruence Between Online Social Content and Future IT Skill Demand

J Mahdavimoghaddam, N Krishnaswamy… - Proceedings of the ACM …, 2021 - dl.acm.org
The speed of digital transformation has resulted in new challenges for job seekers to
become lifelong learners and to develop new skills faster than before. In this paper, our main …

Social user interest mining: methods and applications

F Zarrinkalam, H Fani, E Bagheri - Proceedings of the 25th ACM …, 2019 - dl.acm.org
he abundance of user generated content on social networks pro-vides the opportunity to
build models that are able to accurately and effectively extract, mine and predict users' …

Extracting, mining and predicting users' interests from social networks

F Zarrinkalam, H Fani, E Bagheri - … of the 42nd International ACM SIGIR …, 2019 - dl.acm.org
The abundance of user generated content on social networks provides the opportunity to
build models that are able to accurately and effectively extract, mine and predict users' …

Temporal latent space modeling for community prediction

H Fani, E Bagheri, W Du - … Retrieval: 42nd European Conference on IR …, 2020 - Springer
We propose a temporal latent space model for user community prediction in social networks,
whose goal is to predict future emerging user communities based on past history of users' …

Exploring the Utility of Social Content for Understanding Future In-Demand Skills

J Mahdavimoghaddam, A Bahuguna… - Proceedings of the ACM …, 2022 - dl.acm.org
Rapid technological innovations, especially in the information technology space, demand
the workforce to be vigilant by acquiring new skills to remain relevant and employable. The …