The commodity recommendation algorithm for automatic security sale system based on the internet of things

The objective of this paper is to investigate the promptness of recommendation data in automated product recommendation systems, enhancing their ability to facilitate product promotions and compute user similarities. This study also aims to streamline and refine the product purchasing process. To ease the user’s decision-making, an innovative design for the user terminal’s official account is proposed, creating an effective communication link between users and automated sales products. Second, based on the traditional user-based collaborative filtering recommendation algorithm, user characteristics, time factors, and promotion functions are integrated into the product recommendation algorithm model. To ensure the timeliness of recommendation information and avoid resource waste caused by expired products, time factors and promotion functions are incorporated into the recommendation algorithm model. Finally, a comprehensive test is conducted on the performance of the product recommendation system designed in this experiment. The results show that the average absolute error of the recommended model is reduced by 4.3%, and the improvement effect is significant. The average accuracy rate is increased by about 3.0% compared with the traditional recommendation algorithm. This paper aims to provide important technical support for the improvement of the product recommendation algorithm in the automatic sale system. Besides, this paper provides users with good recommendation services and convenient product purchase processes.