Enhancing Recommender Systems with NLP-based Biased Singular Value Decomposition
Hong Li Xu · 2023
This research introduces a novel Natural Language Processing (NLP) based Biased Singular Value Decomposition (SVD) Recommender System that addresses the limitations of existing methods while maintaining high-quality recommendations. By incorporating advanced NLP techniques and Biased SVD, this paper introduces a controlled bias that accounts for both user and item biases, effectively enhancing the performance of the recommender system while maintaining a certain degree of fairness and transparency. In addition, neural network-driven sequential models are incorporated to enhance the recommender system's overall performance. These approaches contribute to the development of efficient, fair, and transparent recommender systems that cater to the diverse needs and preferences of users in the digital era.