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.

翻译：社交物联网（SIoT）正彻底改变我们与日常生活的交互方式。通过为设备连接增添社交维度，SIoT有望大幅改变我们与智能设备互动的方式。这种互联基础设施提供了前所未有的便利性、自动化和信息获取能力，使人们能够以更少的努力完成更多事情。然而，这项革命性新技术也迫切需要对服务推荐系统的需求。随着SIoT在范围和复杂性上的增长，企业、个人以及SIoT对象自身越来越需要针对其特定需求的产品、服务和信息可靠来源。目前已有少量研究提出为SIoT环境提供服务推荐，但这些工作仅限于利用上下文信息、设备的SIoT关系及相关社交群体建模用户-项目交互，尚未考虑SIoT环境中稀疏多模态内容下潜在的语义项目-项目结构。本文提出一种基于潜在特征的SIoT推荐系统，该系统通过学习项目-项目结构并聚合多模态信息获取潜在项目图，随后通过图卷积将高阶亲和性注入项目表示。实验表明，所提出的推荐系统优于当前最先进的SIoT推荐方法，并验证了其在从多模态特征中挖掘潜在关系的有效性。