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.
Digital advertising is when we pay to place messages in front of consumers across digital channels such as search, social media, and websites.
Like traditional advertising, digital advertising is pay to play:
You spend money to have your ad creative placed in front of consumers.
Unlike traditional advertising, however, digital advertising is a game played at light speed:
Ad space is bought and sold in real-time in auctions regulated by sophisticated machine learning algorithms.
Advertisers have the power to target granular audience segments using rich data from ad platforms on demographics and behaviors.
Platforms like Google and Facebook continually place, adjust, boost, and penalize ads based on ad quality and engagement.
Digital advertising gives brands an unprecedented ability to target, reach, and convert prospects at scale.
But there's a big problem...
This year, Artificial Intelligence has the ability to transform the marketing and advertising ecosystem as we know it. The World’s top 3 “Cool Vendors in AI for Marketing” were released in Gartner’s annual review October 5th, 2020, naming SoMin.ai, Cheq, and Qwarry as the world’s most innovative solutions utilizing AI to solve marketing’s biggest pain points. Previously, Gartner’s list featured Zoom, Movie, Cybereason, Snowflake, Gong, Datorama, Wrike, and many other leading SaaS unicorns.
Ads are everywhere. From expensive billboards to car door stickers, ads occupy every visible surface wherever we go. At the turn of the century, advertisers began to shift their focus online.
In the span of twenty years, we established a thriving ecosystem where brands can reach their targeted consumers in increasingly creative ways across the web.
Whether it is Facebook ads, Spotify banners, or pop-up notifications, consumers today are bombarded by ads nearly every second of every day.
Whilst receiving relevant ads may value-add to our busy lives, it does get a little annoying when you see that same pair of sneakers you searched for just once start appearing across all the web applications that you use.
In the end, cheap advertising that simply shoves products and services in consumers’ faces stand a higher chance of backfiring on their advertisers instead.