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Aleksandr Farseev

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4 papers

1 author row

AAAI Conference 2019 Short Paper

A Whole New Ball Game: Harvesting Game Data for Player Profiling

Ivan Samborskii
Aleksandr Farseev
Andrey Filchenkov
Tat-Seng Chua

Nowadays, video games play a very important role in human life and no longer purely associated with escapism or entertainment. In fact, gaming has become an essential part of our daily routines, which give rise to the exponential growth of various online game platforms. By participating in such platforms, individuals generate a multitude of game data points, which, for example, can be further used for automatic user profiling and recommendation applications. However, the literature on automatic learning from the game data is relatively sparse, which had inspired us to tackle the problem of player profiling in this first preliminary study. Specifically, in this work, we approach the task of player gender prediction based on various types of game data. Our initial experimental results inspire further research on user profiling in the game domain.

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TIST Journal 2017 Journal Article

Learning User Attributes via Mobile Social Multimedia Analytics

Liqiang Nie
Luming Zhang
Meng Wang
Richang Hong
Aleksandr Farseev
Tat-Seng Chua

Learning user attributes from mobile social media is a fundamental basis for many applications, such as personalized and targeting services. A large and growing body of literature has investigated the user attributes learning problem. However, far too little attention has been paid to jointly consider the dual heterogeneities of user attributes learning by harvesting multiple social media sources. In particular, user attributes are complementarily and comprehensively characterized by multiple social media sources, including footprints from Foursqare, daily updates from Twitter, professional careers from Linkedin, and photo posts from Instagram. On the other hand, attributes are inter-correlated in a complex way rather than independent to each other, and highly related attributes may share similar feature sets. Towards this end, we proposed a unified model to jointly regularize the source consistency and graph-constrained relatedness among tasks. As a byproduct, it is able to learn the attribute-specific and attribute-sharing features via graph-guided fused lasso penalty. Besides, we have theoretically demonstrated its optimization. Extensive evaluations on a real-world dataset thoroughly demonstrated the effectiveness of our proposed model.

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AAAI Conference 2017 Short Paper

Towards User Personality Profiling from Multiple Social Networks

Kseniya Buraya
Aleksandr Farseev
Andrey Filchenkov
Tat-Seng Chua

The exponential growth of online social networks has inspired us to tackle the problem of individual user attributes inference from the Big Data perspective. It is well known that various social media networks exhibit different aspects of user interactions, and thus represent users from diverse points of view. In this preliminary study, we make the first step towards solving the significant problem of personality profiling from multiple social networks. Specifically, we tackle the task of relationship prediction, which is closely related to our desired problem. Experimental results show that the incorporation of multi-source data helps to achieve better prediction performance as compared to single-source baselines.

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AAAI Conference 2017 Conference Paper

TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning

Aleksandr Farseev
Tat-Seng Chua

Wellness is a widely popular concept that is commonly applied to ﬁtness and self-help products or services. Inference of personal wellness-related attributes, such as body mass index or diseases tendency, as well as understanding of global dependencies between wellness attributes and users’ behavior is of crucial importance to various applications in personal and public wellness domains. Meanwhile, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness proﬁling for users from multiple perspectives. However, research efforts on wellness proﬁling and integration of social media and sensor data are relatively sparse, and this study represents one of the ﬁrst attempts in this direction. Speciﬁcally, to infer personal wellness attributes, we proposed multi-source individual user proﬁle learning framework named “TweetFit”. “TweetFit” can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. Our experimental results show that the integration of the data from sensors and multiple social media sources can substantially boost the wellness proﬁling performance.

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