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
“Click here to download our mobile app”—or similar phrasing—is a suggestion consumers see more and more often these days on company websites and in marketing emails. With more and more of us opting to conduct our online business on mobile devices, it’s not surprising that so many companies are developing dedicated mobile apps and are eager to promote them.
In fact, tech experts say that there are many industries and business functions that absolutely should have a mobile app not only to be competitive but also (and more importantly) to provide better, faster and more accessible service. Below, 16 members of Forbes Technology Council share the industries and business functions that definitely need a mobile app, and why.
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.