With a large number of possible smart objects in Social Internet of Things (SIoT), a recommendation system is of great necessity to help users find smart objects they need. However, traditional recommendation techniques usually exploit user’s rating or feedback information, which are impractical as such kind of user preference information is difficult to collect in the SIoT environment. In addition, temporal context plays an important role in smart object recommendation since most users tend to utilize different objects at different time slots in a day, e.g., making coffee at morning and playing games on weekends. In this article, we propose a time-aware smart object recommendation model by jointly considering user’s preference over time and smart object’s social similarity. We first learn user’s preference over time from his/her object usage events with a latent probabilistic model. Then, we estimate the smart object’s social similarity by embedding their heterogeneous social relationships into a shared lower dimensional space. Finally, we generate the recommendation list with an item-based collaborative filtering. We conduct a comprehensive experimental study based on two real-world data sets, and the experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.

中文翻译：

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社交物联网中的时间感知型智能对象推荐

社交物联网（SIoT）中拥有大量可能的智能对象，因此非常有必要推荐系统来帮助用户找到所需的智能对象。但是，传统的推荐技术通常会利用用户的评分或反馈信息，这是不切实际的，因为这种用户偏好信息很难在SIoT环境中收集。此外，时间上下文在智能对象推荐中也起着重要作用，因为大多数用户倾向于在一天中的不同时间段使用不同的对象，例如，在早上喝咖啡和在周末玩游戏。在本文中，我们通过共同考虑用户对时间的偏好以及智能对象的社会相似性，提出了一种可感知时间的智能对象推荐模型。我们首先通过潜在的概率模型从对象使用事件中了解用户随时间的偏好。然后，我们通过将智能对象的异构社会关系嵌入到共享的较低维度空间中来估计智能对象的社会相似性。最后，我们使用基于项目的协作过滤来生成推荐列表。我们基于两个实际数据集进行了全面的实验研究，实验结果表明，在推荐效果方面，我们的方法明显优于所有基线。