Building an Urban Smart Community System Based on Association Rule Algorithms

Abstract

Intelligent system development is an integral component of smart community development and has a significant impact on the development of smart communities. Some cities continue to implement personalized smart community services, resulting in the formation of smart city communities with unique characteristics. Urban smart communities are based on the principle of owner-occupant convenience, integrating a wealth of community information and making it more relevant to each and every resident through intelligent management. Increasing information transmission rates have enhanced the ability of smart community systems to integrate information, but the smart community recommendation method is still based on traditional categorized recommendations. This paper addresses the deficiency of recommended information in smart urban communities. By analyzing user interaction and operation data, we can determine the interest and recognition of browsing attractions among users. Compared to conventional classification recommendations, weighted association rules can identify potentially very important rules applicable to small groups, thereby meeting the needs of various groups and enabling personalized services. Through continuous feedback from user behavior data, the system gradually identifies the community information that users are interested in during the specific recommendation process. After testing, the smart community system’s recommendation accuracy and real-time performance have vastly improved in comparison to categorical recommendations, and it can effectively meet the needs of tenants for community recommendations.