Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms

Abstract

In the era of big data, personalized book recommendations have become crucial for enhancing user satisfaction and improving information retrieval efficiency. This study addresses the limitations of existing book recommendation algorithms by proposing a novel Hybrid Book Recommendation Algorithm Considering Different Preferences (HBRACDP). Our approach integrates Capsule Networks and Self-Attention Mechanisms to model both short-term and long-term user borrowing preferences. We construct separate models for these preferences and combine them using a controllable multi-interest network with label attention. Experimental results on the Goodreads dataset demonstrate the superiority of HBRACDP, achieving an accuracy of 0.984, recall of 0.987, and F1 score of 0.988 in ablation tests. In practical scenarios with 1000 students, HBRACDP significantly outperformed traditional algorithms, with a recommendation accuracy of 97.89% and an error rate of only 0.08%. This study provides new insights for developing more accurate and efficient big data recommendation systems in library services and beyond.