Service matchmaking for Internet of Things based on probabilistic topic model

The Internet of Things (IoT) is one of the most rapidly growing technologies which enables things to interact with each other through a global network of machines and devices. In order to facilitate the user to query, search and discover appropriate services, service matchmaking is a critical and challenging task in the IoT. Currently, semantic modeling methods are mainly used to solve service matchmaking problems. However, as the text for service description is often short, these methods face many challenges such as sparse and high-dimensional features. To solve these issues, we propose a service matchmaking method based on Weighted-Word Latent Dirichlet Allocation (WW-LDA). By distinguishing and incorporating the importance of different words, WW-LDA is proposed as a probabilistic topic model to extract latent semantic factors. Based on the result of WW-LDA, we further design a new service matchmaking algorithm to discover IoT services. Experimental results show that our proposed method performs much better than other existing methods in terms of the precision rate and recall rate on real-world datasets.