Reading Recommendation Technology in Digital Libraries Based on Readers' Social Relationships and Readers' Interests

In recent years, the construction of digital libraries has contributed to the advancement of smart lending services. The challenge of suggesting appropriate books for readers from a vast collection of books remains a primary obstacle in the current construction of digital libraries. A fusion method for recommending content to readers with diverse interests is proposed. The method initially extracts short-term borrowing behavior characteristics and simultaneously considers the social similarity characteristics of readers, resulting in the recommendation of content through target ranking search. Aiming to cater to long-term readers, a reading recommendation method that integrates readers' reading behaviors is proposed to model readers' interests through the attention mechanism. It constructs readers' preference models by using synergistic metrics, and finally achieves content recommendation through preference fusion. The proposed model attained the swiftest convergence and the minimum logarithmic loss of 1.85 in recommending readings for multi-interest readers. Additionally, the accuracy of the proposed model in recommending science reading scenarios was 97.24%, surpassing other models. In the reading recommendation experiments for extended borrowings, the suggested model demonstrated superior performance with regard to recall and precision, which were 0.198 and 0.062, respectively. Lastly, after comparing the recommendation errors of different reading models, the proposed model exhibited a root-mean-square error and an average absolute error of 0.731 and 0.721, respectively. These results denote the most precise recommendation accuracy among the three models. The proposed model demonstrates excellent recommendation effectiveness in real-world reading recommendation scenarios. This research offers significant technical references for the advancement of related recommendation technology and the development of digital libraries.

@article{Zheng2024,
  title     = {Reading Recommendation Technology in Digital Libraries Based on Readers' Social Relationships and Readers' Interests},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Weiying Zheng},
  doi       = {10.14569/IJACSA.2024.0150767},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150767}
}