Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet, social networks and the internet of things, the social internet of things has increasingly become a significant research issue. In the social internet of things application scenario, one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources. Although a variety of recommendation algorithms have been employed in this field, they ignore the massive text resources in the social internet of things, which can effectively improve the effect of recommendation. In this paper, a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed. The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the “thing-thing” relationship information in the internet of things to improve the effect of recommendation. Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.