Social media platforms are often used by people to express their needs and desires. Such
data offer great opportunities to identify users' consumption intention from user-generated …

We propose models based on SVM, Naïve Bayes and deep learning to solve the
consumption intention classification problem. Applying consumption intention mining to …

Online social media services, such as Facebook and Twitter, have recently increased in
popularity. Although determining the subjects of individual posts is important for extracting …

For online life service platforms, such as Meituan, user consumption intention, as the internal
driving force of consumption behaviors, plays a significant role in understanding and …

The social media revolution has provided the online community an opportunity and facility to
communicate their views, opinions and intentions about events, policies, services and …

Detecting product adoption intentions on social media could yield significant value in a wide
range of applications, such as personalized recommendations and targeted marketing. In …

Consumption intent, defined as the decision-driven force of consumption behaviors, is
crucial for improving the explainability and performance of user-modeling systems, with …

Abstract Estimating Click-through Rate (CTR) and Conversion Rate (CVR) are two essential
user response prediction tasks in computing advertising and recommendation systems. The …

Predictive analytics is being increasingly used to predict various aspects of applications and
users. It offers vast opportunities in the growth of the modern era's business transformation …

Click-through rate (CTR) prediction is a critical task in online advertising systems. A large
body of research considers each ad independently, but ignores its relationship to other ads …