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.
In this technical demonstration, we showcase the first ai-driven social multimedia influencer discovery marketplace, called SoMin. The platform combines advanced data analytics and behavioral science to help marketers find, understand their audience and engage the most relevant social media micro-influencers at a large scale. SoMin harvests brand-specific life social multimedia streams in a specified market domain, followed by rich analytics and semantic-based influencer search. The Individual User Profiling models extrapolate the key personal characteristics of the brand audience, while the influencer retrieval engine reveals the semantically-matching social media influencers to the platform users. The influencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By leveraging influencers at a large scale, marketers will be able to execute more effective marketing campaigns of higher trust and at a lower cost.
The exponential growth of online social networks has inspired us to tackle the problem of individual user attributes inference from the Big Data perspective. It is well known that various social media networks exhibit different aspects of user interactions, and thus represent users from diverse points of view. In this preliminary study, we make the first step towards solving the significant problem of personality profiling from multiple social networks. Specifically, we tackle the task of relationship prediction, which is closely related to our desired problem. Experimental results show that the incorporation of multi-source data helps to achieve better prediction performance as compared to single-source baselines.
The technological revolution marked by the shift from using a mouse and keyboard to touch screens has transformed our interaction with devices. In this rapidly evolving landscape, humans communicating with familiar applications using natural language has emerged as the most intuitive and effortless solution to bridge the gap between humans and machines, making life easier for customers and expanding the market. OpenAI, a trailblazer in AI development and advocacy, has ventured into the realm of venture capitalism through its subsidiary, the OpenAI Startup Fund, dispersing substantial investments to four promising DeepTech startups: Descript, Harvey, Mem, and Speak. Three of these companies harness the power of large language models (LLMs) to revolutionize human-machine interaction, raising the question: Why were these particular startups selected out of thousands of companies worldwide?
The technological revolution marked by the shift from using a mouse and keyboard to touch screens has transformed our interaction with devices. In this rapidly evolving landscape, humans communicating with familiar applications using natural language has emerged as the most intuitive and effortless solution to bridge the gap between humans and machines, making life easier for customers and expanding the market. OpenAI, a trailblazer in AI development and advocacy, has ventured into the realm of venture capitalism through its subsidiary, the OpenAI Startup Fund, dispersing substantial investments to four promising DeepTech startups: Descript, Harvey, Mem, and Speak. Three of these companies harness the power of large language models (LLMs) to revolutionize human-machine interaction, raising the question: Why were these particular startups selected out of thousands of companies worldwide?
As technology continues to evolve and become more integrated into our daily lives—and as the internet and social media have opened up new ways for consumers to publicly voice their opinions on products—the user experience has become a critical factor in the success of any tech product. Companies are now focusing on providing seamless and intuitive experiences that cater to their users’ needs and preferences.
This growing emphasis on UX has led to new trends expected to become table stakes in the next five years. Below, 16 Forbes Technology Council members explore some of the upcoming UX trends that will be crucial for the success of tech products and why they will be so important.