Digital Library Book Recommendation Model Based on Collaborative Filtering and Cloud Computing - Wang, Xiujing; Zhang, Chuanyu; Wu, Jinming (Informatica, 2024)
With the rapid development of the Internet, digital resources are growing exponentially. In this context, more libraries are transforming into smart libraries. The demand for detailed project recommendations and real-time updates is becoming increasingly prominent due to information redundancy caused by excessive data. Therefore, this study attempts to build a digital library data platform based on cloud computing. In the traditional recommendation algorithm, attention mechanisms in deep learning are introduced. A neural collaborative filtering algorithm based on channel attention is proposed, and an improved digital library book recommendation model is designed by combining the two. The test results showed that the average value, optimal value, and standard deviation of the improved algorithm were the lowest among the compared algorithms. The loss function value was the lowest. The area under the curve was 0.85. When the final recommended number of books was 5, 10, 15, and 20, the recommendation accuracy and coverage were both above 90%, with an average time of 0.28s. The mean reciprocal ranking for different proportions of experimental population was 0.393. It demonstrates that the proposed book recommendation model for digital libraries has higher accuracy, stable recommendation effects, and less time consumption. The library recommendation model built using this algorithm can effectively provide targeted and personalized recommendation services to users.