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 …
H Li, Y Xiao, C Zheng, P Wu… - … Conference on Machine …, 2023 - proceedings.mlr.press
Propensity-based weighting methods have been widely studied and demonstrated competitive performance in debiased recommendations. Nevertheless, there are still many …
Z Chen, J Wu, C Li, J Chen, R Xiao… - Proceedings of the 45th …, 2022 - dl.acm.org
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 …
H Li, C Zheng, P Wu - The Eleventh International Conference on …, 2023 - researchgate.net
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 …