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.
Leaders often want to get tasks done themselves to ensure quality and timely completion, but no leader can do it all. Delegating certain tasks to other members of your team frees up time so that you can get more accomplished and continue to grow in your leadership position. And as tech leaders play increasingly large roles in companies, it’s essential for them to embrace the art of delegation.
IT leaders have a lot of unique opportunities to delegate work, from automating repetitive tasks to appointing someone to oversee product management. Here, 14 members of Forbes Technology Council share some tasks IT leaders should get off their plates as soon as possible.
Not too long ago, I was asked to present a tool to some of my clients. It was a simple prototype, where a person would type in a few things (i.e., advertising channel, product and occasion), and in turn, the machine would give a number of sample ads. When I clicked the button, in just a few seconds, the machine spat out several ads complete with images and text. The first comment was, “Wow, that was really fast.” What would take a person a few hours to do, this machine did in but a fraction. There were a lot of other interesting comments, some even pointing out that this machine was really creative. Then one person spoke out, a comment that put the room into an uncomfortable silence, “This thing is going to take my job.”
Many of today’s consumers prefer using digital payment methods such as Apple Pay, PayPal or Venmo to make the purchasing process more convenient. As a business owner, accepting these types of payments can signal to customers that you have a modern, streamlined checkout process. However, customers may lose trust in your brand if your digital payment system isn’t working properly or if there is ever a security breach.
If you do decide to accept digital payment methods, there are a few important things to consider and set up first. Below, a panel of Forbes Technology Council members offers their best advice for ensuring a secure and easy digital payment process.