Online advertising, as a vast market, has gained significant attention in various platforms
ranging from search engines, third-party websites, social media, and mobile apps. The …

Multimodal signals are powerful for emotion recognition since they can represent emotions
comprehensively. In this article, we compare the recognition performance and robustness of …

Multimodal emotion recognition, that is, emotion recognition uses machine learning to
generate multi-modal features on the basis of videos which has become a research hotspot …

Personalized recommendation serves as a ubiquitous channel for users to discover
information tailored to their interests. However, traditional recommendation models primarily …

Understanding human behaviors and intents from videos is a challenging task. Video flows
usually involve time-series data from different modalities, such as natural language, facial …

Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy
has a direct impact on user experience and platform revenue. In recent years, CTR …

Sequential recommendation aims to offer potentially interesting products to users by
capturing their historical sequence of interacted items. Although it has facilitated extensive …

Recommendation systems (RS) have become prevalent across different domains including
music, e-commerce, e-learning, entertainment, and social media to address the issue of …

Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by
the Internet users on various social media platforms. On one hand, existing studies focus on …

As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention
from both academia and industry. Recent studies have been reported to establish CTR …