Research on E-Commerce User Interest Recommendation Method Based on TF-IDF Algorithm

In the development process of the e-commerce industry, with the continuous increase of the number of consumer user groups, the base of online shopping user groups is getting larger and larger. In this process, the research on recommendation algorithms for different user groups is very important. The current research has made some progress, but there are still some problems, such as the data classification for user portraits is not specific enough, the feature extraction algorithm has related deficiencies, and the data recommendation accuracy is not enough. On this basis, this paper adjusts the word frequency and inverse document frequency of the recommended keywords. It introduces the TF-IDF algorithm to recommend targeted product keywords for different user portraits, effectively solving inaccurate recommendation results.