Existing neural collaborative filtering (NCF) recommendation methods suffer from severe
sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected …

Recommender systems suggest items that users might like according to their explicit and
implicit feedback information, such as ratings, reviews, and clicks. However, most …

To address the sparsity and cold-start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve the …

Despite the variety of sensors that can be used in a smart home or office setup, for
monitoring energy consumption and assisting users to save energy, their usefulness is …

With the rapid development of online social recommendation system, substantial methods
have been proposed. Unlike traditional recommendation system, social recommendation …

Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various
tasks, but we cannot accurately know the importance of different design dimensions of …

Heterogeneous information network (HIN) embedding has gained increasing interests
recently. However, the current way of random-walk based HIN embedding methods have …

User purchasing prediction with multi-behavior information remains a challenging problem
for current recommendation systems. Various methods have been proposed to address it via …

Due to its severe damages and threats to the security of the Internet and computing devices,
malware detection has caught the attention of both anti-malware industry and researchers …

The explosive growth of machine learning (ML) methods is overloading users with choices
for learning tasks. Method recommendation aims to alleviate this problem by selecting the …