A Book Recommendation Algorithm for High Vocational School Library Based on Deep Learning
Yingying Tang, Gao Lin, Xiuzhu Ruan, Yi Qing Zhu · 2024
Aiming at the many shortcomings of collaborative filtering algorithms in traditional recommender systems, such as cold-start and data sparsity, a book recommendation algorithm based on deep learning is proposed. Firstly, multiple data sources are fused to collect readers' information and books' information to enrich readers' side features and books' side features; then the deep learning model is utilized to extract readers' embedding features and books' embedding features; then the dot product of the two is used as the output, which is used as the basis for providing recommendations to users; finally, this paper conducts experiments by comparing with the ALS algorithm. The experimental results show that the model proposed in this paper improves the data sparsity and cold-start problems, and improves the book recommendation efficiency.