Personalized Recommendation for Online News Based on UBCF and IBCF Algorithms

With the popularization of the Internet and the widespread use of mobile devices, online news has become one of the main ways for people to obtain information and understand the world. However, the increasing number and variety of news often cause users to feel troubled when searching for content of interest. To solve this problem, the first step is to design a personalized recommendation model for online news. Based on this model, a new personalized recommendation model is designed by combining the item-based collaborative filtering (IBCF) and the user-based collaborative filtering (UBCF). The experimental results showed that the average scores of the volunteers for the performance indicators, coverage indicators, and satisfaction indicators of the model were 85 and 93, 86, respectively. This system has high accuracy, low resource consumption, and higher user satisfaction, providing a new algorithmic approach for the field of recommendation models. The contribution of research is not only improving the accuracy of recommendations, but also increasing the diversity of recommendations, effectively solving the problem of data sparsity and real-time news. By introducing a tag propagation network for clustering analysis of users and projects, the recommendation results are further optimized and user satisfaction is improved. In addition, the research also realizes efficient data processing and storage through real-time user data collection and distributed data processing technology, which significantly improves the performance and response speed of the system.

@article{Shi2025,
  title     = {Personalized Recommendation for Online News Based on UBCF and IBCF Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Wei Shi and Yitian Zhang},
  doi       = {10.14569/IJACSA.2025.0160434},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160434}
}