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@article{farseev2017tweetCanBeFit,
                        title={Tweet can be Fit: Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning},
                        author={Farseev, Aleksandr and Chua, Tat-Seng},
                        journal={ACM Transactions on Information Systems (TOIS)},
                        year={2017},
                        publisher={ACM}
                }
}
              
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@article{nie2017learning,
                        titlee={Learning user attributes via mobile social multimedia analytics},
                        author={Nie, Liqiang and Zhang, Luming and Wang, Meng and Hong, Richang and Farseev, Aleksandr and Chua, Tat-Seng},
                        journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
                        volume={8},
                        number={3},
                        pages={36},
                        year={2017},
                        publisher={ACM}
                }
}
              
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@inproceedings{farseev2017cross,
                        title={Cross-domain recommendation via clustering on multi-layer graphs},
                        author={Farseev, Aleksandr and Samborskii, Ivan and Filchenkov, Andrey and Chua, Tat-Seng},
                        booktitle={Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval},
                        pages={195--204},
                        year={2017},
                        organization={ACM}
                }
}
              
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@article{farseev2016360,
                    title={360° user profiling: past, future, and applications by Aleksandr Farseev, Mohammad Akbari, Ivan Samborskii and Tat-Seng Chua with Martin Vesely as coordinator},
                    author={Farseev, Aleksandr and Akbari, Mohammad and Samborskii, Ivan and Chua, Tat-Seng},
                    journal={ACM SIGWEB Newsletter},
                    number={Summer},
                    pages={4},
                    year={2016},
                    publisher={ACM}
}
                
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@inproceedings{farseev2015harvesting,
                    title={Harvesting multiple sources for user profile learning: a big data study},
                    author={Farseev, Aleksandr and Nie, Liqiang and Akbari, Mohammad and Chua, Tat-Seng},
                    booktitle={Proceedings of the 5th ACM on International Conference on Multimedia Retrieval},
                    pages={235--242},
                    year={2015},
                    organization={ACM}
}
                
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@inproceedings{farseev2015cross,
                    title={Cross-Social Network Collaborative Recommendation},
                    author={Farseev, Aleksandr and Kotkov, Denis and Semenov, Alexander and Veijalainen, Jari and Chua, Tat-Seng},
                    booktitle={Proceedings of the ACM International Conference on Web Science (WebSci)},
                    year={2015}
                    organization={ACM}
}
                
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@inproceedings{farseev2017tweetFit,
                    title={TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning},
                    author={Farseev, Aleksandr and Chua, Tat-Seng},
                    booktitle={Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},
                    year={2017},
                    organization={AAAI}
}
                
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@inproceedings{buraya2017personality,
                    title={Towards User Personality Profiling from Multiple Social Networks},
                    author={Buraya, Kseniya and Farseev, Aleksandr and Filchenkov, Andrey and Chua, Tat-Seng},
                    booktitle={Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence},
                    year={2017},
                    organization={AAAI}
}
                
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@inproceedings{farseev2016bbridge,
                    title={bBridge: A Big Data Platform for Social Multimedia Analytics},
                    author={Farseev, Aleksandr and Samborskii, Ivan and Chua, Tat-Seng},
                    booktitle={Proceedings of the 24rd ACM international conference on Multimedia},
                    year={2016},
                    organization={ACM}
}
                
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Publications
A. Farseev, I. Samborskii, A. Filchenkov, and T.-S. Chua. Cross-Domain Recommendation via Clustering on Multi-Layer Graphs 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'17), August 7-11, 2017.
L. Nie, L. Zhang, M. Wang, R. Hong, A. Farseev, and T.-S. Chua. Learning User Attributes via Mobile Social Multimedia Analytics ACM Transactions on Intelligent Systems and Technology (TIST), 8 (3), 36, 2017.
A. Farseev and T.-S. Chua. Tweet can be Fit: Integrating Data from Wearable Sensors and Multiple Social Networks for Wellness Profile Learning ACM Transactions on Information Systems (TOIS), 2017.
A. Farseev and T.-S. Chua. TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning. Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), Feb 4-9, 2017.
K. Buraya, A. Farseev, A. Filchenkov, and T.-S. Chua. Towards user personality profiling from multiple social networks. Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), Feb 4-9, 2017.
A. Farseev, I. Samborskii, and T.-S. Chua. bBridge: A Big Data Platform for Social Multimedia Analytics. ACM Multimedia Conference 2016, Oct. 15 - 19, 2016.
A. Farseev, M. Akbari, I. Samborskii, and T.-S. Chua. 360° User Profiling: Past, Future, and Applications. ACM SIGWEB Newsletter, Summer, 2016.
A. Farseev, N. Liqiang, M. Akbari, and T.-S. Chua. Harvesting multiple sources for user profile learning: a Big data study. ACM International Conference on Multimedia Retrieval (ICMR). China. June 23-26, 2015.
A. Farseev, D. Kotkov, A. Semenov, J. Veijalainen, and T.-S. Chua. Cross-Social Network Collaborative Recommendation. ACM International Conference on Web Science (WebSci) 2015.
А. Фарсеев, Н. Жуков, И. Государев, и Ю. Заричняк. Разработка Кросплатформенной Рекомендательной Системы на Основе Извлечения Данных из Социальных Сетей. Компьютерные Инструменты в Образовании. June 2014.
Presentations
Learning from Multiple Social Networks for Research and Business @ WST NET Web Science Summer School. St. Petersburg, Russia. July. 2-10, 2017.
Summer School on Social Media Computing @ ISMW-FRUCT '16. St. Petersburg, Russia. Aug. 28 2016 - Sept. 4, 2016.
Winter School on Social Media Computing @ AINL-FRUCT '15. St. Petersburg, Russia. Nov. 9-14, 2015.
User Attributes Profiling from multi-source multi-modal data sources @ Computer Science Club POMI Russian Academy of Science (CS клуб ПОМИ РАН). St. Petersburg, Russia. Nov. 8, 2015.
Harvesting multiple sources for user profile learning: a Big data study. @ ACM International Conference on Multimedia Retrieval (ICMR). Shanghai, China. June 23-26, 2015.
Projects
bBridge is the Big Data Analytics Platform that aims to bridge the gap between Social Media Users, Business, and the Big Data. It results in two closely related applications: bBridge for Business, and eTrack: The Mobile App. bBridge for Business offers Real-Time Group Analytics to Business and Public Sector Users. The provided features are: Trade Area Analysis, User Communities Detection and Profiling, Live Social Media Stream Analytics, Hot Topics Extraction, Brand Monitoring. eTrack brings Personal Analytics to Social Media Users. It is a fun App that encourage its users to be engaged into Social Media.
With the rapid growth of multi-source social media resources, comprehensive user profile learning from multiple data sources serves as an actual backbone in various application domains. Such user profile components as user wellness or user demography describe social media users from different views. The goal of the NUS-MSS and NUS-SENSE projects is to develop efficient data analysis and integration techniques for multi-source user profile learning.