Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Multi-domain recommendation (MDR) aims to improve the recommendation performance in
all target domains simultaneously by leveraging rich data from relevant domains. However …

Multi-domain recommendation (MDR) aims to provide recommendations for different
domains (eg, types of products) with overlapping users/items and is common for platforms …

Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …

Multi-behavior recommendations effectively integrate various types of behaviors and have
been proven to enhance recommendation performance. However, existing researches …

Recommender systems (RS) have been widely used in Web applications such as search
engines, social media platforms and e-commerce portals. Accuracy-related metrics have …

E-commerce platforms should provide detailed product descriptions (attribute values) for
effective product search and recommendation. However, attribute value information is …

Abstract Information extraction aims to automatically extract structured information from
unstructured texts. Supervised information extraction requires large quantities of labeled …