Contents I. Introduction
The competition in the e-commerce industry is becoming intense. To stand out in the market, enterprises need to provide more personalized and accurate services. With the development of e-commerce, a personalized recommendation system has become an important tool to improve user stickiness and promote sales. However, the traditional recommendation system shows insufficient accuracy in analyzing user behavior and large-scale product data. To solve this problem, artificial intelligence (AI) technology was used with collaborative filtering algorithms to design a personalized recommendation system in this study [1]. Through the normalization of the user rating data and the construction of the order matrix, the system accurately captured the user’s personalized recommendation system. It is an effective means of attracting and retaining users in e-commerce platforms. The personalized recommendation system was designed from the perspective of the user’s needs. Through in-depth analysis of the user’s behavioral data and the use of advanced AI technology, an efficient and intelligent recommendation system was constructed with scalability and protection ability. User privacy can be protected to ensure the stability of the system and the security of the user’s information [2, 3].
