Cutting-edge recommendation algorithms have been widely used by media platforms to
suggest users with personalized content. While such user-specific recommendations may …

Graph neural networks (GNNs) have shown great potential for personalized
recommendation. At the core is to reorganize interaction data as a user-item bipartite graph …

Item features play an important role in movie recommender systems, where
recommendations can be generated by using explicit or implicit preferences of users on …

Personalized video recommender systems play an essential role in bridging users and
videos. However, most existing video recommendation methods assume that user profiles …

This Natural language processing, Computer vision, and speech recognition are among the
fields in which deep learning outperforms prior approaches. The majority of deep learning …

Personalized recommendation plays a central role in many online content sharing platforms.
To provide quality micro-video recommendation service, it is of crucial importance to …

Short-form video sharing mobile applications like TikTok (Douyin) have been gaining
traction globally in recent years. These video sharing platforms have transformed how users …

Deep learning is known to be effective at automating the generation of representations,
which eliminates the need for handcrafted features. For the task of personalized …

Micro-videos have recently gained immense popularity, sparking critical research in micro-
video recommendation with significant implications for the entertainment, advertising, and e …

Recommender systems are prone to be misled by biases in the data. Models trained with
biased data fail to capture the real interests of users, thus it is critical to alleviate the impact …