Papers by Aleksandr Farseev
Human personality traits are the key drivers behind our decisionmaking, influencing our life path... more Human personality traits are the key drivers behind our decisionmaking, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called "PERS" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction. CCS CONCEPTS • Computing methodologies → Machine learning; • Applied computing → Psychology.
Proceedings of the ... AAAI Conference on Artificial Intelligence, Apr 3, 2020
Nowadays, social networks play a crucial role in human everyday life and no longer purely associa... more Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
We present SoCraft, an advertiser-level multimedia ad content scoring platform for Meta Ads. SoCr... more We present SoCraft, an advertiser-level multimedia ad content scoring platform for Meta Ads. SoCraft utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high-and low-level features of its context data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore. CCS CONCEPTS • Information systems → Learning to rank; Multimedia and multimodal retrieval; Computational advertising;
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
Digital Advertising is historically one of the most developed areas where Machine Learning and AI... more Digital Advertising is historically one of the most developed areas where Machine Learning and AI have been applied since its origination. From smart bidding to creative content generation and DCO, AI is well-demanded in the modern digital marketing industry and partially serves as a backbone of most of the state-of-theart computational advertising systems, making them impossible for the AI tech and the programmatic systems to exist apart from one another. At the same time, given the drastic growth of the available AI technology nowadays, the issue of responsible AI utilization as well as the balance between the opportunity of deploying AI systems and the possible borderline etic and privacy-related consequences are still yet to be discussed comprehensively in both business and research communities. Particularly, an important issue of automatic User Profiling use in modern Programmatic systems like Meta Ads as well as the need for responsible application of the creative assessment models to fit into the business etic guidelines is yet to be described well. Therefore, in this talk, we are going to discuss the technology behind modern programmatic bidding and content scoring systems and the responsible application of AI by SoMin.ai to manage the Advertising targeting and Creative Validation process. • Information systems → Computational advertising.
Proceedings of the 30th ACM International Conference on Multimedia
Social media marketing plays a vital role in promoting brand and product values to wide audiences... more Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. Still, McKinsey-style manual categorization is a very labour-intensive task that is probably impractical in a real-world scenario, so automated incorporation of audience behaviour and personality mining into industrial applications is necessary. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Even worse, there is no dataset available for the research community to serve as a benchmark and drive further research in this direction. The present study is one of the first attempts to bridge this important industrial gap, contributing not just a novel personality-driven content recommendation approach and dataset, but also facilitating a real-world ready solution which is scalable and sufficiently accurate to be applied in real-world settings. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable
Do we behave differently on Twitter and Facebook: Multi-view social network user personality profiling for content recommendation
Frontiers in Big Data
Human personality traits are key drivers behind our decision making, influencing our lives on a d... more Human personality traits are key drivers behind our decision making, influencing our lives on a daily basis. Inference of personality traits, such as the Myers-Briggs personality type, as well as an understanding of dependencies between personality traits and user behavior on various social media platforms, is of crucial importance to modern research and industry applications such as recommender systems. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse; the impact of different social network data on profiling performance and of personality traits on applications such as recommender systems is yet to be evaluated. Furthermore, large-scale datasets are also lacking in the research community. To fill these gap...
Proceedings of the AAAI Conference on Artificial Intelligence
Wellness is a widely popular concept that is commonly applied to fitness and self-help products o... more 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 senso...
Proceedings of the AAAI Conference on Artificial Intelligence
The exponential growth of online social networks has inspired us to tackle the problem of individ... more 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.
360 User Profile Learning from Multiple Social Networks for Wellness and Urban Mobility Applications
Ph.DDOCTOR OF PHILOSOPH
ArXiv, 2020
Nowadays, social networks play a crucial role in human everyday life and no longer purely associa... more Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when be...
2017 IEEE Life Sciences Conference (LSC), 2017
Effective classification of physical exercises allows individuals to assess their levels of physi... more Effective classification of physical exercises allows individuals to assess their levels of physical activity and functional ability for maintaining physical fitness and help reduce risks of chronic diseases. This paper investigates and compares classification techniques for detecting physical exercise in real-world contexts that often only supports a small training dataset. The system combines heart rate with other exercise-related features, such as distance, duration, calories, etc. The experiment uses a dataset of 40 realistic (uncontrolled) sessions from 22 individuals wearing wearable sensors while performing different exercises, including walking, aerobics, running, indoor cycling, and weight training. Based on a 5-fold cross validation approach, AdaBoost demonstrated the highest (87.25%) classification accuracy compared to other classifiers, including support vector machine, neural network, and binary decision tree when used individually. When fused together at the decision level using majority-voting techniques, these classifiers achieved higher accuracy (89.25%) than that of individual applications.
Multimedia Understanding with Less Labeling on Multimedia Understanding with Less Labeling, 2021
Automatic user post classification is an important task in the field of social network analysis. ... more Automatic user post classification is an important task in the field of social network analysis. Being effectively solved, post classification could be used for thematic user feed composition or inappropriate content identification. Commonly addressed by applying various Machine Learning approaches, the task often involves manual processes related to ground truth sourcing, which is known to be a hardly-scalable and increasingly expensive procedure. At the same time, Active Learning for automatic user post classification is a promising way to bridge such a gap, as it does not require massive ground truth availability aligning our research with the real world settings. In this work, we put our focus on leveraging textual and visual data modalities for the application of user post classification and investigate how batch size and batch normalization disabling techniques could affect active deep neural network learning process. We solve the problem of automatic user post classification by employing our novel multimodal neural network architecture with multi-head tunable loss function components. We show that the proposed approach, coupled with Active Learning, allows for the achievement of a significant classification performance boost in terms of crowd assessing resources as compared to the passive learning approaches. CCS CONCEPTS • Information systems → Clustering and classification; Multimedia and multimodal retrieval; • Computing methodologies → Neural networks; Active learning settings.
Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval, 2021
Human personality traits are the key drivers behind our decisionmaking, influencing our life path... more Human personality traits are the key drivers behind our decisionmaking, influencing our life path on a daily basis. Inference of personality traits, such as Myers-Briggs Personality Type, as well as an understanding of dependencies between personality traits and users' behavior on various social media platforms is of crucial importance to modern research and industry applications. The emergence of diverse and cross-purpose social media avenues makes it possible to perform user personality profiling automatically and efficiently based on data represented across multiple data modalities. However, the research efforts on personality profiling from multi-source multi-modal social media data are relatively sparse, and the level of impact of different social network data on machine learning performance has yet to be comprehensively evaluated. Furthermore, there is not such dataset in the research community to benchmark. This study is one of the first attempts towards bridging such an important research gap. Specifically, in this work, we infer the Myers-Briggs Personality Type indicators, by applying a novel multi-view fusion framework, called "PERS" and comparing the performance results not just across data modalities but also with respect to different social network data sources. Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources. We have also found that the selection of a machine learning approach is of crucial importance when choosing social network data sources and that people tend to reveal multiple facets of their personality in different social media avenues. Our released social multimedia dataset facilitates future research on this direction. CCS CONCEPTS • Computing methodologies → Machine learning; • Applied computing → Psychology.
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021
In this technical demonstration, we showcase the World's first personality-driven marketing conte... more In this technical demonstration, we showcase the World's first personality-driven marketing content generation platform, called SoMin.ai [7]. The platform combines deep multi-view personality profiling framework and style generative adversarial networks facilitating the automatic creation of content that appeals to different human personality types. The platform can be used for enhancement of the social networking user experience as well as for content marketing routines. Guided by the MBTI personality type, automatically derived from a user social network content, SoMin.ai generates new social media content based on the preferences of other users with a similar personality type aiming at enhancing the user experience on social networking venues as well diversifying the efforts of marketers when crafting new content for digital marketing campaigns. The real-time user feedback to the platform via the platform's GUI fine-tunes the content generation model and the evaluation results demonstrate the promising performance of the proposed multi-view personality profiling framework when being applied in the content generation scenario. By leveraging content generation at a large scale, marketers will be able to execute more effective digital marketing campaigns at a lower cost.
Proceedings of the 26th ACM international conference on Multimedia, 2018
In this technical demonstration, we showcase the first ai-driven social multimedia influencer dis... more In this technical demonstration, we showcase the first ai-driven social multimedia influencer discovery marketplace, called SoMin [4]. The platform combines advanced data analytics and behavioral science to help marketers find, understand their audience and engage the most relevant social media micro-influencers at a large scale. SoMin harvests brand-specific life social multimedia streams in a specified market domain, followed by rich analytics and semanticbased influencer search. The Individual User Profiling models extrapolate the key personal characteristics of the brand audience, while the influencer retrieval engine reveals the semantically-matching social media influencers to the platform users. The influencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By leveraging influencers at a large scale, marketers will be able to execute more effective marketing campaigns of higher trust and at a lower cost.
Lecture Notes in Computer Science, 2018
Personality profiling is an essential application for the marketing, advertisement and sales indu... more Personality profiling is an essential application for the marketing, advertisement and sales industries. Indeed, the knowledge about one's personality may help in understanding the reasons behind one's behavior and his/her motivation in undertaking new life challenges. In this study, we take the first step towards solving the problem of automatic personality profiling. Specifically, we propose the idea of fusing multisource multi-modal temporal data in our computational "PersonalL-STM" framework for automatic user personality inference. Experimental results show that incorporation of multi-source temporal data allows for more accurate personality profiling, as compared to non-temporal baselines and different data source combinations.
BackgroundThe rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted li... more BackgroundThe rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely populated areas.ObjectiveAiming at bringing more light on key economic and population health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly available COVID-19 datasets.MethodsWe have applied Pearson Correlation Analysis and Clustering Analysis (X-Means Clustering) techniques on the data obtained by combining multiple datasets related to country economics, medical system & health, and COVID-19 - related statistics. The resulting dataset consisted of COVID-19 Case and Mortality Rates, Economic Statistics, and Population Public Health Statist...
ACM Transactions on Intelligent Systems and Technology, 2017
Learning user attributes from mobile social media is a fundamental basis for many applications, s... more 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 t...
ACM Transactions on Information Systems, 2017
Wellness is a widely popular concept that is commonly applied to fitness and self-help products o... more 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 (BMI) category or disease 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. At the same time, 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. This study represents one of the first attempts in this direction. Specifically, we infer personal wellness attributes by utilizing our proposed multisource multitask wellness profile learning framework—WellMTL—which can handle data incompleteness and perform wellness attributes inference from sensor and...
Proceedings of the 24th ACM international conference on Multimedia, 2016
In this technical demonstration, we propose a cloud-based Big Data Platform for Social Multimedia... more In this technical demonstration, we propose a cloud-based Big Data Platform for Social Multimedia Analytics called bBridge [9] that automatically detects and profiles meaningful user communities in a specified geographical region, followed by rich analytics on communities' multimedia streams. The system executes a community detection approach that considers the ability of social networks to complement each other during the process of latent representation learning, while the community profiling is implemented based on the state-of-the-art multi-modal latent topic modeling and personal user profiling techniques. The stream analytics is performed via cloud-based stream analytics engine, while the multi-source data crawler deployed as a distributed cloud jobs. Overall, the bBridge platform integrates all the above techniques to serve both business and personal objectives.
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Papers by Aleksandr Farseev