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.
User profile learning, such as mobility and demographic profile learning, is of great importance to various applications. Meanwhile, the rapid growth of multiple social platforms makes it possible to perform a comprehensive user profile learning from different views. However, the research efforts on user profile learning from multiple data sources are still relatively sparse, and there is no large-scale dataset released towards user profile learning. In our study, we contribute such benchmark and perform an initial study on user mobility and demographic profile learning. First, we constructed and released a large-scale multi-source multimodal dataset from three geographical areas. We then applied our proposed ensemble model on this dataset to learn user profile. Based on our experimental results, we observed that multiple data sources mutually complement each other and their appropriate fusion boosts the user profiling performance.
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 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. 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, and this study represents one of the first attempts in this direction. Specifically, we infer personal wellness attributes by utilizing our proposed multi-source multitask wellness profile learning framework — “WellMTL”, which can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. To gain insights into the data at a global level, we also examine correlations between first-order data representations and personal wellness attributes. Our experimental results show that the integration of sensor data and multiple social media sources can substantially boost the performance of individual wellness profiling.
The digital media platforms of today, such as Facebook and Google, allow businesses countless options to reach their intended audiences. Through their AI-driven technologies, they promise the capability of influencing customers' lives at any given time or place. Yet, within this limitless potential, there lies a problem.
"With hundreds of thousands of targeting options and millions of data outputs, it becomes humanly impossible to fully utilize these platforms. While media platforms are truly powerful, a bottleneck occurs in the interaction between the platforms and the businesses that utilize them, " says Hendrik Schwartz, Co-founder and CRO of SoMin.ai.
March 9 saw the launch of the principal phase of Sber500, Sber and 500 Startups’ international accelerator program. The 25 best startups were chosen to work individually with Silicon Valley mentors over the next eight weeks and present their projects on Demo Day to Sber ecosystem executives and leading Russian and international corporations and funds.
The accelerator received entries from approximately 1,800 startups from countries all over the world – almost three times the applicants for the previous two Sber500 batches. Aside from Russia, this year’s submissions came from countries including Singapore, Israel, Germany, Kazakhstan, Latvia, Belarus, and Estonia. The vast majority of projects submitted (over 70%) have already closed their first deals and are based on B2B and B2B2C business models. Around 60% of all submissions were startups working with e-commerce, logistics, marketing, and Data Analytics.
Artificial intelligence has completely redefined the world we live in today. Now, our mornings start with personal assistants waking us up and laying our favorite playlists. Bots help customers with their questions and issues about the services and products they have purchased. And now, artificial intelligence is capable of redefining influencer marketing with the power of creating a persona.
Meet Maya — the next-generation virtual influencer set to transform the concept of brand marketing.