Modern online platforms are increasingly employing recommendation systems to address
information overload and improve user engagement. There is an evolving paradigm in this …

Contrastive learning, a form of Self-Supervised Learning (SSL), typically consists of an
alignment term and a regularization term. The alignment term minimizes the distance …

Collaborative Filtering~(CF) typically suffers from the significant challenge of popularity bias
due to the uneven distribution of items in real-world datasets. This bias leads to a significant …

Masked Image Modeling (MIM) is a powerful self-supervised strategy for visual pre-training
without the use of labels. MIM applies random crops to input images, processes them with …

Recommendation Systems (RS) are often plagued by popularity bias. When training a
recommendation model on a typically long-tailed dataset, the model tends to not only inherit …

Searching on bipartite graphs serves as a fundamental task for various real-world
applications, such as recommendation systems, database retrieval, and document querying …

Recommender models aimed at mining users' behavioral patterns have raised great
attention as one of the essential applications in daily life. Recent work on graph neural …

Given some video-query pairs with untrimmed videos and sentence queries, temporal
sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although …

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity
problem by enabling knowledge transfer across source and target domains. However, many …

The classic problem of node importance estimation has been conventionally studied with
homogeneous network topology analysis. To deal with practical network heterogeneity, a …