Deep learning-based recommendations have demonstrated impressive performance in
improving recommendation accuracy. However, such approaches mainly utilize implicit …

Recommender systems are essential tools that provide personalized user experiences
across various domains such as e-commerce, entertainment, social media, education and …

This paper introduces a novel method aimed at enhancing online-to-offline (O2O) services
recommendations by utilizing two-layer knowledge networks. The primary objective of this …

Completeness and consistency are widely acknowledged as indispensable prerequisites for
applying fuzzy preference relations in resolving real-world problems. Interactions between …

Multi-behavioral recommender systems have emerged as a solution to address data sparsity
and cold-start issues by incorporating auxiliary behaviors alongside target behaviors …

In existing trust-aware collaborative filtering algorithms, each trust relationship between two
users is usually represented by a real number, but such a number is neither sufficient to …

Knowledge graph (KG) is increasingly important in improving recommendation performance
and handling item cold-start. A recent research hotspot is designing end-to-end models …

Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more
reliable we desire the forecasts, the more conservative the decision will be and thus, the …

Recommender systems are essential tools that provide personalized user experiences
across various domains such as e-commerce, entertainment, social media, education and …