Design and Application of Personalized Recommendation Algorithm for Electricity Marketing

With the rapid development of the current economy and technology, the electricity consumption behavior of power users has shown an unprecedented trend of diversification, with diverse and rapidly changing demand patterns. To effectively respond to this market change, power companies urgently need to transform and upgrade, and use innovative marketing strategies to accurately meet user needs, improve service quality, and enhance market competitiveness. In response to the significant drawbacks of traditional power marketing methods, such as rigid strategies, difficulty in accurately matching personalized user needs, uneven distribution of power resources, and serious waste, this article explores and proposes a personalized recommendation algorithm for power marketing based on big data analysis and artificial intelligence (AI) technology. This algorithm utilizes advanced machine learning (ML) models to achieve precise prediction of users' electricity habits, preferences, and future demand through deep mining of massive user electricity data. The experimental results show that the algorithm proposed in this paper performs well in improving user satisfaction and enhancing user stickiness, while significantly reducing the operating costs of power enterprises and reducing resource waste, providing strong support for the sustainable development of the power industry.