Izvorni znanstveni članak
https://doi.org/10.20532/cit.2024.1005845
Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms
Jiali Liao
; School of Information Engineering, Sichuan Top IT Vocational Institute, Sichuan, China
Tianxiang Li
; School of Information Engineering, Chengdu Industry and Trade College, Sichuan, China
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* Dopisni autor.
Sažetak
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.
Ključne riječi
CNN; SAM; Book recommendations; Users; AM; CN
Hrčak ID:
326534
URI
Datum izdavanja:
16.11.2024.
Posjeta: 0 *