The traditional keyword-based “passive” semantic service search technology in the Internet will no longer be applicable to the internet of things （IoT） environment due to the sharp growth of sensors as well as the frequent change of device state. How to utilize and analyze a large amount of interactive information between users and devices to recommend the most relevant equipment resources according to users’ preference is the key of resource discovery algorithm in IoT. A representation model of user-device interaction based on hypergraph theory was presented and matched with corresponding representation matrix. Based on this model， the resource recommendation problem which can be transformed into a correlation degree prediction problem based on matrix decomposition was formulated. Then the alternating least squares （ALS） method in optimization theory was introduced here to tackle this optimal decomposition problem. Finally， the IoT resource recommendation algorithm based on latent factor model was proposed. The simulation proved that the proposed approach outperformed item-based collaborative filtering （ItemCF） algorithm in terms of root mean square error （RMSE） and mean absolute error （MAE）.