Exploration of Content Personalization Recommendation Optimization
The research purpose of this article is to improve the effectiveness of recommendation algorithms,better meet the personalized needs of users,and provide support for building a better content recommendation system.The article elaborates on the importance,current problems,and limitations of content personalized recommendation algorithms,and proposes corresponding optimization and improvement plans.The current common content recommendation algorithms have problems in cold start,data sparsity,complex relationship modeling,and long-term dependencies.This article explores a series of optimization and improvement solutions to address these issues.Future research can further explore other technical means and data processing methods to further enhance the effectiveness of personalized content recommendation and user satisfaction of content products.
personalized recommendation of contentrecommendation algorithmcold startdata sparsitymodeling complex relationshipslong term dependence
万方数据
维普
