To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Emerging brain-inspired neuromorphic computing systems have become a potential candidate
for overcoming the von Neuman bottleneck that limits the performance of most modern …

Incorporating knowledge graph into recommender systems has attracted increasing attention
in recent years. By exploring the interlinks within a knowledge graph, the connectivity …

User preferences are usually dynamic in real-world recommender systems, and a user»s
historical behavior records may not be equally important when predicting his/her future interests…

Numerous benchmarks have been established to assess the performance of foundation models
on open-ended question answering, which serves as a comprehensive test of a model's …

Iron-based MOFs with tunable structure, large surface area along with attractive diversity
have been often used in heterogeneous Fenton-like reactions to generate hydroxyl groups for …

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed
alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (…

Fashion recommendation has attracted increasing attention from both industry and academic
communities. This paper proposes a novel neural architecture for fashion recommendation …

In this study, FeMg-LDH loaded with bentonite (FeMg-LDH@bentonite) was prepared using
the facile co-precipitating method in situ to remove heavy metals from water and then …