Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale
problems in many areas, such DNNs are hard to be deployed in real-world systems due to …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …

Data-driven recommender systems have demonstrated great success in various Web
applications owing to the extraordinary ability of machine learning models to recognize …

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs)
have been effectively utilized as recommenders, achieving impressive performance …

Previous debiasing studies utilize unbiased data to make supervision of model training.
They suffer from the high trial risks and experimental costs to obtain unbiased data. Recent …

Multimedia online platforms, such as Amazon and TikTok, have greatly benefited from the
incorporation of multimedia content (eg, visual, textual, and acoustic modalities), into their …

Recently published graph neural networks (GNNs) show promising performance at social
event detection tasks. However, most studies are oriented toward monolingual data in …

The distillation of ranking models has become an important topic in both academia and
industry. In recent years, several advanced methods have been proposed to tackle this …

In recent years, recommender systems have achieved remarkable performance by using
ensembles of heterogeneous models. However, this approach is costly due to the resources …