A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things

TL;DR

This paper introduces a multi-modal, latent-feature based recommendation system for the Social Internet of Things that leverages item-item structures and graph convolutions to improve service recommendations.

Contribution

It proposes a novel latent-based approach that models item-item relationships using multi-modal data and graph convolutions, addressing limitations of previous methods.

Findings

Outperforms existing SIoT recommendation methods

Effectively mines latent relationships from multi-modal features

Validated through comprehensive experiments

Abstract

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.