Personalized Recommendation Algorithm for Electronic Commerce Based on Artificial Intelligence Technology

In the era of e-commerce, a large amount of data and complex business models make traditional manual recommendations difficult to adapt to constantly changing personalized needs. Finding a more efficient product recommendation algorithm has become an important topic in the field of e-commerce. The scale of online shopping is constantly expanding, and it is necessary to match higher technological investments to explore the value of data. By comparing the advantages, disadvantages, and application scenarios of collaborative filtering algorithms and artificial intelligence technology, this article constructs a personalized recommendation system for e-commerce according to data mining. The results show that the recall rate of AI based recommendation algorithms is about 0.1 higher than that of collaborative filtering-based recommendation algorithms, and after using artificial intelligence algorithms for personalized recommendations, eBay’s recommendation accuracy has improved by about 8.26%. The recommendation system designed in this article can provide recommendations for products or services that meet users’ needs according to their interests as well as preferences, thereby improving their shopping experience.