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.
As a professor of machine learning, I often get asked how I think artificial intelligence (AI) will change the world we’re living in. To answer this question, I usually find it easier to speak about one of the faster-moving fields of AI—digital advertising, in which AI has been around for quite some time but has always sat on the side of big tech. Now, as technology is becoming more available, you can see how it can potentially change the way we do things.
When setting out on a journey, it’s always wise to have a reliable roadmap. The same holds true when it comes to embarking on new tech projects and initiatives. And just as there’s considerable flexibility in terms of routes and guides when planning a trip, tech leaders and project managers have several choices when it comes to tools and techniques for plotting and refining a project roadmap.
With so many options available, a young tech leader or first-time project manager can benefit from the advice and experience of seasoned industry leaders. Below, 15 members of Forbes Technology Council share some roadmapping tools and techniques they’ve found effective and why they work so well.
While there’s more focus than ever on providing a good user interface/user experience in new tech tools, that doesn’t just mean adding lots of “wow” functionality. To ensure the best UX, it’s important for hardware and software developers to also pay attention to ergonomic design. A focus on ergonomics ensures that the tool is adapted to foster comfort and convenience for the human user—that is, the human should inform how the tool works, not the other way around.
While the concept is simple enough, implementing it can be challenging, as designers must take account of factors ranging from device sizes to color palettes. Here, 16 members of Forbes Technology Council share tips to help developers ensure they’ve incorporated good ergonomic design principles in their products.