ارائه یک سیستم توصیه‌گر ترکیبی هوشمند مبتنی بر داده‌کاوی برای شخصی‌سازی خدمات در کتابخانه‌های دیجیتال

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه پیام نور، تهران، ایران.

2 استادیار، گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه پیام نور، تهران، ایران

چکیده

با رشد فزاینده منابع دیجیتال، کاربران کتابخانه‌های دیجیتال با چالش‌های قابل توجهی در یافتن مؤثر و سریع منابع مرتبط مواجه هستند. روش‌های سنتی جستجو و فیلتر، اغلب نیازهای شخصی‌سازی‌شده کاربران را برآورده نمی‌کنند و به همین دلیل سیستم‌های توصیه‌گر هوشمند اهمیت ویژه‌ای یافته‌اند. این پژوهش یک سیستم توصیه‌گر ترکیبی ارائه می‌دهد که با بهره‌گیری هم‌زمان از داده‌های رفتاری و مبتنی بر محتوا، خدمات شخصی‌سازی‌شده را در کتابخانه‌های دیجیتال ارتقا می‌دهد. چارچوب پیشنهادی شامل پیش‌پردازش داده‌ها، دو موتور توصیه‌گر (محتوایی و مشارکتی) و یک لایه ادغام هوشمند است که خروجی‌ها را ترکیب کرده و پیشنهادهای دقیق، متنوع و قابل اعتماد تولید می‌کند. داده‌های رفتاری از مجموعه داده Book-Crossing و اطلاعات محتوایی از منابع Book-Crossing و CiteULike استخراج شد تا ترجیحات کاربران و ویژگی‌های محتوایی منابع مدل‌سازی شود. عملکرد سیستم با معیارهای دقت، پوشش، تنوع و رضایت کاربر مبتنی بر ارزیابی هوشمند ارزیابی شد و نتایج نشان داد که روش ترکیبی نسبت به مدل‌های تک‌رویکرد، دقت و پوشش بالاتری دارد. یافته‌ها نشان می‌دهند که این رویکرد علاوه بر کاهش مشکلاتی مانند «شروع سرد» و سوگیری محبوبیت، موجب بهبود تجربه کاربری و بهینه‌سازی بهره‌برداری از منابع کتابخانه‌های دیجیتال می‌شود و پتانسیل بالای ترکیب داده‌کاوی و تحلیل معنایی متن را در نسل بعدی بازیابی اطلاعات نشان می‌دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Hybrid Intelligent Recommender System Based on Data Mining for Personalizing Services in Digital Libraries

نویسندگان [English]

  • Peyman Almasinejad 1
  • Mohammad Zahaby 2
1 Assistant Professor, Department of Knowledge and Information Science, Payame Noor University, Tehran, Iran.
2 Assistant Professor, Department of Knowledge and Information Science, Payame Noor University, Tehran, Iran
چکیده [English]

Introduction
The rapid growth of digital resources has increased the difficulty of identifying relevant information efficiently in digital libraries. Traditional search and filtering approaches are often unable to address users’ diverse and personalized information needs. Intelligent recommender systems have therefore emerged as an important solution for improving information discovery and user experience. However, relying solely on content-based or collaborative filtering may lead to limitations such as the cold-start problem, popularity bias, and insufficient recommendation coverage. Accordingly, this study aims to develop a hybrid intelligent recommender system based on data mining to personalize services in digital libraries. The proposed approach combines users’ behavioral information with content-based features to generate more accurate, diverse, and reliable recommendations.
 
Methodology
This study developed a hybrid recommendation framework consisting of three main components: data preprocessing, two complementary recommendation engines, and an intelligent aggregation layer. The first engine uses content-based information to identify resources with characteristics similar to users’ interests, whereas the second employs collaborative filtering based on users’ behavioral patterns and interactions. The outputs of these two engines are subsequently integrated through an intelligent aggregation mechanism to produce the final recommendations. Behavioral data were obtained from the Book-Crossing dataset, while content-based information was extracted from both the Book-Crossing and CiteULike datasets. The performance of the proposed system was evaluated using precision, recall, diversity, coverage, and a simulated user-satisfaction measure. The evaluation focused on comparing the hybrid approach with single-strategy recommendation methods.
 
Findings
The findings demonstrate that the proposed hybrid recommender system performs better than single-strategy recommendation approaches in terms of recommendation accuracy and coverage. Combining behavioral and content-based information enables the system to provide recommendations that are simultaneously more precise and diverse. The results also indicate that the hybrid framework can alleviate important limitations of conventional recommender systems, particularly the cold-start problem and popularity bias. The integration of data mining with semantic analysis of textual content contributes to more effective modeling of user preferences and resource characteristics. Overall, the experimental results confirm the effectiveness of combining complementary recommendation strategies for improving personalized information services in digital libraries.
 
Discussion and Conclusion
The findings suggest that hybrid recommendation provides an effective framework for addressing the complexity of personalized information retrieval in digital libraries. Integrating collaborative and content-based approaches allows the system to exploit both behavioral patterns and the semantic characteristics of digital resources, thereby improving recommendation quality and coverage. From a practical perspective, the proposed framework can help digital libraries deliver more personalized services, improve users’ information-search experience, and facilitate more effective utilization of available resources. Its scalable and adaptable structure also provides potential for application in next-generation digital library environments. In conclusion, combining data mining, collaborative filtering, and semantic content analysis can provide a practical foundation for developing intelligent and personalized recommendation services in digital libraries.

کلیدواژه‌ها [English]

  • Organizational Effectiveness
  • Professional Ethics
  • Organizational Intelligence
  • Knowledge Management

مراجع

Ansari, M., & Vakili-Mofrad, A. (2021). Data mining techniques in recommender systems: Challenges and applications. Journal of Information Systems Research, 15(2), 45–60.
ArXiv. (2024). Advances in recommender systems from 2017 to 2024: Trends and challenges. arXiv preprint.
Bauden, D., & Robinson, J. A. (2009). Theories of Information Behavior. London: Facet Publishing.
Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2018). Paper recommender systems: A literature survey. International Journal on Digital Libraries, 20(2), 203–223. https://doi.org/10.1007/s00799-018-0235-4
Burke, R. (2022). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370. https://doi.org/10.1023/A:1021240730564
Çano, E., & Morisio, M. (2017). Hybrid recommender systems: Survey and experimental comparison. Expert Systems with Applications, 78, 260–284. https://doi.org/10.1016/j.eswa.2017.02.026
da Silva, F., Oliveira, A., & Rocha, A. (2023). Improving hybrid recommender systems in digital libraries. Information Processing & Management, 60(1), 102845. https://doi.org/10.1016/j.ipm.2022.102845
DeLone, W. H. (2012). Information systems success model: Evaluation and extensions. MIS Quarterly Review, 36(1), 1–20.
Grover, A., et al. (2022). Recommender systems in digital environments: A survey. Journal of Data Science Applications, 18(3), 155–172.
Gue, J., et al. (2010). Evaluation metrics for recommender systems. Proceedings of Information Retrieval Conference, 201–210.
Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann.
http://dx.doi.org/10.61186/armaghanj.29.3.365.
Huang, Z., Chen, H., & Zeng, D. (2002). Applying associative retrieval techniques to alleviate sparsity in collaborative filtering. ACM Transactions on Information Systems, 20(2), 170–205. https://doi.org/10.1145/514281.514286
Ibrahim, R. (2025). Collaborative and content-based recommender systems: A comparative study. Journal of Information Science, 51(3), 456–472. https://doi.org/10.1177/01655515241123456
Jomsri, P., Smith, L., & Ruan, T. (2024). Hybrid recommender systems for digital libraries: Balancing collaborative and content-based approaches. Journal of Library Science, 46(1), 12–29. https://doi.org/10.1016/j.jlis.2024.01.004
Kreutz, D., Beel, J., & Gipp, B. (2022). Systematic review of scientific paper recommender systems. Information, 13(7), 332. https://doi.org/10.3390/info13070332
Lee, T., & Mikolov, T. (2014). Distributed representations of sentences and documents. Proceedings of ICML, 1188–1196.
Lops, P., de Gemmis, M., & Semeraro, G. (2011). Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook (pp. 73–105). Springer.
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
Philip, R., Zhang, L., & Singh, A. (2025). Content-based paper recommendation using semantic feature extraction. Information Processing & Management, 62(2), 103013. https://doi.org/10.1016/j.ipm.2024.103013
Rhanoui, M., Boulaalam, A., & El Ghaoui, L. (2022). Hybrid recommender system for acquisition and weeding decisions in libraries. Library Hi Tech, 40(4), 678–695. https://doi.org/10.1108/LHT-12-2021-0298
Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (2015). Recommender systems handbook (2nd ed.). Springer.
Rodpysh, A. (2021). Cold start problem in recommender systems: Challenges and solutions. Journal of Big Data, 8(1), 59. https://doi.org/10.1186/s40537-021-00471-3
Sabiri, H. (2025). Hybrid recommender systems for big data: Opportunities and challenges. Journal of Information Science, 51(2), 178–195. https://doi.org/10.1177/01655515241112345
Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 1–19. https://doi.org/10.1155/2009/421425
Wang, Y., & Blei, D. M. (2011). Collaborative topic modeling for recommending scientific articles. Proceedings of KDD, 448–456.
Wang, Y., Li, H., & Zhao, Q. (2024). Mining user behavior and text content for book recommendation in digital libraries. Expert Systems with Applications, 214, 119088. https://doi.org/10.1016/j.eswa.2024.119088
Xu, Z., Chen, Y., & Lin, S. (2022). Large-scale user behavior mining for recommender systems in digital libraries. Information Processing & Management, 59(6), 103017. https://doi.org/10.1016/j.ipm.2022.103017
Yadav, P., Singh, R., & Sharma, V. (2022). SPACE-R: Scalable and precise academic recommender for large collections. Journal of Information Science, 48(5), 622–639. https://doi.org/10.1177/01655515211012345
Zahaby, M. , Boroumandzadeh, M. and Makhdoom, I. (2025). Deep Learning-Based Decision Fusion for Breast Cancer Classification Using Multi-Source Medical Data. Control and Optimization in Applied Mathematics, 10(2), 51-73. https://doi.org/10.30473/coam.2025.73974.1295.
Zahaby, M., Shiri, M.E., Haj Seyed Javadi, H., Broumandzadeh, M. (2024). Automatic classification of BI-RADS in mammography reports using data fusion. Armaghane Danesh, 29(3), 365-85,
Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005). Improving recommendation lists through topic diversification. Proceedings of the 14th International Conference on World Wide Web  - WWW ’05, 22. https://doi.org/10.1145/1060745.1060754

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سابقه مقاله

  • تاریخ دریافت: 05 فروردین 1405
  • تاریخ بازنگری: 10 اردیبهشت 1405
  • تاریخ پذیرش: 12 خرداد 1405
  • تاریخ اولین انتشار: 12 خرداد 1405

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