Recommendation Algorithm Based on Large Language Model: Consideration on Algorithm Governance and User Rights Protection

With the rapid development of big data and artificial intelligence technology, recommendation systems play a crucial role in improving user experience, increasing platform revenue, and optimizing content distribution. However, the wide application of recommendation system also brings about problems such as filter bubble, algorithm black box, labor rights protection, the big data-enabled price discrimination and so on. By processing massive data, the large language model can accurately construct and predict user interests and preferences, process and generate diverse content, thus improving the relevance and fairness of recommendation, enhancing the diversity and transparency of recommendation system, protecting the rights and interests of workers, preventing price discrimination, and realizing algorithm governance and the protection of user rights and interests. This paper designs a recommendation algorithm framework based on a large language model, aiming to build a fair and transparent recommendation system, which can reduce information bias, improve the transparency of the list, protect the rights and interests of workers, prevent price discrimination, and promote social equity.