The Social Internet of Things (SIoT), is revolutionizing how we interact with
our everyday lives. By adding the social dimension to connecting devices, the
SIoT has the potential to drastically change the way we interact with smart
devices. This connected infrastructure allows for unprecedented levels of
convenience, automation, and access to information, allowing us to do more with
less effort. However, this revolutionary new technology also brings an eager
need for service recommendation systems. As the SIoT grows in scope and
complexity, it becomes increasingly important for businesses and individuals,
and SIoT objects alike to have reliable sources for products, services, and
information that are tailored to their specific needs. Few works have been
proposed to provide service recommendations for SIoT environments. However,
these efforts have been confined to only focusing on modeling user-item
interactions using contextual information, devices' SIoT relationships, and
correlation social groups but these schemes do not account for latent semantic
item-item structures underlying the sparse multi-modal contents in SIoT
environment. In this paper, we propose a latent-based SIoT recommendation
system that learns item-item structures and aggregates multiple modalities to
obtain latent item graphs which are then used in graph convolutions to inject
high-order affinities into item representations. Experiments showed that the
proposed recommendation system outperformed state-of-the-art SIoT
recommendation methods and validated its efficacy at mining latent
relationships from multi-modal features