Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms
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 Cons...
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| Format: | Article |
| Language: | English |
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University of Zagreb Faculty of Electrical Engineering and Computing
2024-01-01
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| Series: | Journal of Computing and Information Technology |
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| Online Access: | https://hrcak.srce.hr/file/471975 |
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| author | Jiali Liao Tianxiang Li |
| author_facet | Jiali Liao Tianxiang Li |
| author_sort | Jiali Liao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c44f44fef94745f68db59dc52594be4e |
| institution | Kabale University |
| issn | 1846-3908 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb Faculty of Electrical Engineering and Computing |
| record_format | Article |
| series | Journal of Computing and Information Technology |
| spelling | doaj-art-c44f44fef94745f68db59dc52594be4e2025-01-09T14:17:11ZengUniversity of Zagreb Faculty of Electrical Engineering and ComputingJournal of Computing and Information Technology1846-39082024-01-0132317719410.20532/cit.2024.1005845Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention MechanismsJiali Liao0Tianxiang Li1School of Information Engineering, Sichuan Top IT Vocational Institute, Sichuan, ChinaSchool of Information Engineering, Chengdu Industry and Trade College, Sichuan, ChinaIn 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.https://hrcak.srce.hr/file/471975CNNSAMBook recommendationsUsersAMCN |
| spellingShingle | Jiali Liao Tianxiang Li Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms Journal of Computing and Information Technology CNN SAM Book recommendations Users AM CN |
| title | Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms |
| title_full | Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms |
| title_fullStr | Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms |
| title_full_unstemmed | Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms |
| title_short | Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms |
| title_sort | leveraging deep learning for personalized book recommendations a big data algorithm combining capsule networks and attention mechanisms |
| topic | CNN SAM Book recommendations Users AM CN |
| url | https://hrcak.srce.hr/file/471975 |
| work_keys_str_mv | AT jialiliao leveragingdeeplearningforpersonalizedbookrecommendationsabigdataalgorithmcombiningcapsulenetworksandattentionmechanisms AT tianxiangli leveragingdeeplearningforpersonalizedbookrecommendationsabigdataalgorithmcombiningcapsulenetworksandattentionmechanisms |