Close [x]
                    @article{samborskii2019game,
                        title={A Whole New Ball Game: Harvesting Game Data for Player Profiling},
                        author={Samborskii, Ivan and Farseev, Aleksandr and Filchenkov, Andrey and Chua Tat-Seng},
                        booktitle={Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence},
                        year={2019},
                        organization={AAAI}
}
                }
Close [x]
                    @article{buraya2018personality,
                        title={Multi-View Personality Profiling Based on Longitudinal Data},
                        author={Buraya, Kseniya and Farseev, Aleksandr and Filchenkov, Andrey},
                        booktitle={International Conference of the Cross-Language Evaluation Forum for European Languages},
                          year={2018},
                          organization={Springer}
}
                }
Close [x]
                    @inproceedings{farseev2018somin,
                    author = {Aleksandr, Farseev and Kirill, Lepikhin and Hendrik, Schwartz and Eu Khoon, Ang and Kenny, Powar},
                    title = {SoMin.ai: Social Multimedia Influencer Discovery Marketplace},
                    booktitle={Proceedings of the 26th ACM International Conference on Multimedia},
                     series = {MM '18},
                     year = {2018},
                     isbn = {978-1-4503-5665-7/18/10},
                     url = {http://doi.acm.org/10.1145/3240508.3241387},
                     doi = {10.1145/3240508.3241387},
                     publisher = {ACM},
}
Close [x]
                    @phdthesis{farseev2017360,
                        title={360 USER PROFILE LEARNING FROM MULTIPLE SOCIAL NETWORKS FOR WELLNESS AND URBAN MOBILITY APPLICATIONS},
                        author={FARSEEV, ALEKSANDR},
                        year={2017}
                    }
Close [x]
                    @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},
                        journall={ACM Transactions on Information Systems (TOIS)},
                        year={2017},
                        publisher={ACM}
                }
Close [x]
                    @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}
                        }
Close [x]
                    @inproceedings{chowdhury2017automatic,
                       title={Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings},
                        author={Chowdhury, Alok Kumar and Farseev, Aleksandr and Chakraborty, Prithwi Raj and Tjondronegoro, Dian and Chandran, Vinod},
                        booktitle={Life Sciences Conference (LSC), 2017 IEEE},
                        pages={111--114},
                        year={2017},
                        organization={IEEE}
                        }
Close [x]
                    @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}
                    }
Close [x]
                    @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}
                    }
Close [x]
                    @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}
                    }
Close [x]
                    @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}
                    }
Close [x]
                    @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}
                    }
Close [x]
                    @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}
                    }
Close [x]
                    @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}
                    }
Close [x]
Aleksandr Farseev
Research Professor; CEO at SoMin.ai
Prof. Aleks Farseev is an Entrepreneur, International Keynote Speaker, and the CEO of SoMin.ai, the Long-Tail AI Advertisement Targeting and Optimization Platform on Meta and Google. Known to be an expert in Digital and Influencer Marketing, Aleks has also co-authored over 30 scientific publications in top-ranked peer-reviewed journals and conferences. Aleks holds a Research Professor position at multiple universities across Asia and Europe and had successfully conducted university courses and training sessions on Digital Marketing, Influencer Marketing, and AI Technology.
#Multi-source user profiling #Cross-Social data gathering #Urban mobility analysis
Publications
I. Samborskii, A. Farseev, A. Filchenkov, T.-S. Chua A Whole New Ball Game: Harvesting Game Data for Player ProfilingThirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Jan 27 – Feb 1, 2019.
A. Farseev, K. Lepikhin, H. Schwartz, E. K. Ang, K. Powar. SoMin.ai: Social Multimedia Influencer Discovery MarketplaceACM Multimedia Conference 2018, Oct. 22 - 26, 2018.
K. Buraya, A. Farseev, & A. Filchenkov. Multi-View Personality Profiling Based on Longitudinal Data International Conference of the Cross-Language Evaluation Forum for European Languages (CLEF), 2018.
A. Farseev PhD Thesis: 360 User Profile Learning from Multiple Social Network for Wellness and Urban Mobility Applications National University of Singapore, 2017.
A. K. Chowdhury, A. Farseev, P. R. Chakraborty, D. Tjondronegoro, & V. Chandran. Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings. IEEE Life Sciences Conference (LSC), 2017.
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
SoMin is the Social Multimedia Analytics Platform that aims to bridge the gap between Social Media Users, Business, and the Big Data. The backbone technology is SoMin User Profiling API, which is able to predict Personality, Age, Gender, Education Level, Relationship Status, Income, Education, Emotional Profile, and the Interests of Social Media users from their-posted multimedia content. Based on the detected profiles, SoMin's Customer Segmentation and Content Recommendation AI engines will then answer the Questions: HOW emotional, WHICH content must be posted to WHO and WHEN in order to maximize and extremely personalize Social Media Marketing Message. The message will be then delivered via top-matched Social Media Micro-Influencers and Advertisement Platforms.
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.
Contacts