Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

With the increasing popularity of location-aware social media services, next-Point-of-Interest
(POI) recommendation has gained significant research interest. The key challenge of next …

Subspace learning decreases the dimensions for high-dimensional data by projecting the
original data into a low-dimensional subspace, as well as preserving the similarity among …

Existing point-of-interesting (POI) recommendation methods lack sufficient integration of
information related to the features of individual users and their corresponding contexts …

Abstract Location-based Social Networks (LBSNs) enable users to socialize with friends and
acquaintances by sharing their check-ins, opinions, photos, and reviews. A huge volume of …

Self-supervised representation learning (SSRL) concerns the problem of learning a useful
data representation without the requirement for labelled or annotated data. This …

Abstract Next Point-Of-Interest (POI) recommendation aim to predict users' next visits by
mining their movement patterns. Existing works attempt to extract spatial–temporal …

Topic-based communities have gradually become a considerable medium for netizens to
disseminate and acquire knowledge. These communities consist of entities (actual objects …

For the safe operation of complex equipment, it is essential to implement accurate anomaly
detection on the key parts of equipment. However, due to the extreme conditions of the …

Prior studies of paper recommendation methods that consider historical user preferences
rarely adequately address the complexity of user preferences and interests. We propose a …