Enhanced Content-based Recommendation Method

The intuitive and easily demonstrable benefits of content-based recommendation technology have made it widely adopted in education and e-commerce. However, traditional content-based recommendation methods face a challenge when it comes to analysis with regard to item modeling. These methods fail to consider the need for data parallelism technique upon large contents while producing structured data items. The objective of this research is to enhance the content analysis capability of traditional content-based recommendation algorithms by incorporating a multithreaded information vector space model for item modeling during content retrieval. Several experiments were carried out to assess the efficacy of this approach. The findings from these experiments indicate that the algorithm proposed in this paper exhibits satisfactory performance across different scenarios