Research on Personalized Recommendation Algorithms Based on User Profile

Abstract

In recent decades, recommendation systems (RS) have played a pivotal role in societal life, closely intertwined with people's everyday activities. However, traditional recommendation systems still require thorough consideration of comprehensive user profiles as they have struggled to provide more personalized and accurate recommendation services. This paper delves into the analysis and enrichment of user profiles, utilizing this foundation to tailor recommendations for individuals across domains such as movies, TV shows, and books. The paper constructs a chart comprising 246 types of user profile attributes, primarily covering dimensions like gender, age, occupation, and religious beliefs, among 16 other dimensions. This chart integrates approximately 1.2 million data points, encompassing information relevant to movies, TV shows, and novels. Through training on the dataset, the study has enhanced the model's recommendation effectiveness. Post-training, the recommendation accuracy surpasses that of pre-training based on proposed evaluation metrics. Furthermore, post-manual evaluation, the recommended results are more reasonable and align better with user profiles.