Most of the recent studies of social recommendation assume that people share similar
preferences with their friends and the online social relations are helpful in improving …

Shop** without much experience on target items is not unusual in social commerce (s-
commerce). Inexperienced users are often influenced by user reviews when making …

Attributes are important auxiliary information for representing user and item features,
especially in data-sparse scenario, and can serve as their main source. However, different …

Most of the extant studies in social recommender system are based on explicit social
relationships, while the potential of implicit relationships in the heterogeneous social …

The Social Internet of Things (SIoT), integration of the Internet of Things, and Social
Networks paradigms, has been introduced to build a network of smart nodes that are …

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 …

With the rapid advancement of Internet, e-commerce websites and social networks, people
prefer to receive recommendations from their social friends rather than strangers. Also, the …

To deal with the inherent data sparsity and cold-start problem, many recommender systems
try to exploit the textual information for improving prediction accuracy. Due to the significant …

Betweenness centrality is a prominent centrality measure expressing importance of a node
within a network, in terms of the fraction of shortest paths passing through that node. Nodes …

Social recommendation has become an important technique of various online commerce
platforms, which aims to predict the user preference based on the social network and the …