Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System

The information construction work of university libraries is becoming increasingly perfect. However, the massive amount of data poses significant challenges to the personalized recommendation of books. Cluster analysis has always been an important research topic in data mining technology, and it has a wide range of application fields. Clustering algorithm is a fundamental operation in big data processing, and it also has good application value in personalized recommendation of library books. To improve the personalized service quality of libraries, this study proposes a clustering algorithm based on density noise application spatial clustering. This study introduced a distance optimization strategy and Warhill algorithm to the proposed algorithm, to improve the difficulties in selecting initial parameter neighborhoods and density thresholds in traditional models, as well as computational complexity. Afterwards, this study will integrate the improved algorithm with the density peak algorithm to further improve the operational efficiency of the model. The performance verification of the model demonstrated superior clustering performance. The average accuracy of the proposed model's recommendation is 98.97%, indicating superiority. The practical application results have confirmed that there is a significant similarity between the books read by the readers and the books read by the target readers, and the effectiveness and feasibility of the proposed model have been verified. Therefore, the proposed model can contribute to the personalized recommendation function of libraries and has certain practical significance.

@article{Lin2023,
  title     = {Application of Data Mining Technology with Improved Clustering Algorithm in Library Personalized Book Recommendation System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Xiao Lin and Wenjuan Guan and Ying Zhang},
  doi       = {10.14569/IJACSA.2023.0141151},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141151}
}