Ranking, recommendation, and retrieval systems are widely used in online platforms and
other societal systems, including e-commerce, media-streaming, admissions, gig platforms …

Recommender system usually faces popularity bias issues: from the data perspective, items
exhibit uneven (usually long-tail) distribution on the interaction frequency; from the method …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …

We tackle the task of video moment retrieval (VMR), which aims to localize a specific
moment in a video according to a textual query. Existing methods primarily model the …

Recommender systems (RSs) aim at helping users to effectively retrieve items of their
interests from a large catalogue. For a quite long time, researchers and practitioners have …

Propensity-based weighting methods have been widely studied and demonstrated
competitive performance in debiased recommendations. Nevertheless, there are still many …

Recommender system usually faces popularity bias. From the popularity distribution shift
perspective, the normal paradigm trained on exposed items (most are hot items) identifies …

Recently, recommender system (RS) based on causal inference has gained much attention
in the industrial community, as well as the states of the art performance in many prediction …

In recommender systems, users always choose the favorite items to rate, which leads to data
missing not at random and poses a great challenge for unbiased evaluation and learning of …