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.
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.
Venue category recommendation is an essential application for the tourism and advertisement industries, wherein it may suggest attractive localities within close proximity to users’ current location. Considering that many adults use more than three social networks simultaneously, it is reasonable to leverage on this rapidly growing multi-source social media data to boost venue recommendation performance. Another approach to achieve higher recommendation results is to utilize group knowledge, which is able to diversify recommendation output. Taking into account these two aspects, we introduce a novel cross-network collaborative recommendation framework C 3R, which utilizes both individual and group knowledge, while being trained on data from multiple social media sources. Group knowledge is derived based on new crosssource user community detection approach, which utilizes both inter-source relationship and the ability of sources to complement each other. To fully utilize multi-source multi-view data, we process user-generated content by employing state-of-the-art text, image, and location processing techniques. Our experimental results demonstrate the superiority of our multi-source framework over state-of-the-art baselines and different data source combinations. In addition, we suggest a new approach for automatic construction of inter-network relationship graph based on the data, which eliminates the necessity of having pre-defined domain knowledge
Most tech pros closely follow tech news and developments because of their passion for the field. However, tech leaders can’t rely solely on that innate curiosity to ensure their team members stay up to date on the latest industry developments. It’s essential to set aside time and provide resources for team members to explore cutting-edge, relevant tech trends to ensure your company remains competitive.
Creating a culture of learning not only contributes to your tech team members’ happiness and growth, but also ensures your team will continue to build products and services that customers truly need and want. Below, 16 members of Forbes Technology Council detail the ways they ensure their teams stay ahead of the game in terms of new technology and trends.
From the classic novels of Isaac Asimov to the Star Wars franchise on the big screen, robots have been a fixture of futuristic science fiction for decades. Today, robots and robotic process automation are taking on new roles in both the business and consumer worlds. Innovations in robotics and RPA can expand the capabilities of humans and organizations, safely manage work and events that entail physical risk, and free up humans for more creative, fulfilling work.
Tech experts say advanced robots and RPA may soon revolutionize health care, environmental stewardship and even international travel. Here, 16 members of Forbes Technology Council share new and upcoming robotics innovations that could truly make a big difference in the way we live and work.
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.