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<p>Recommendation systems are crucial in managing data overload, enabling online platforms to provide users with personalized recommendations. However, these systems often encounter significant challenges, such as the cold start problem and data sparsity, which hinder recommendation accuracy. To address these issues effectively, this study proposes an innovative approach that leverages implicit knowledge of users and items, structured into three primary stages. First, we employ clustering techniques to segment the user base, which reduces data volume and mitigates sparsity, enhancing the system's ability to deliver accurate recommendations. In the second phase, Association Rule Mining (ARM) analyses users' implicit interaction records, allowing us to derive valuable association rules and better understand user preferences. Finally, in the third stage, the system leverages these insights to suggest optimal items to each user, enhancing personalization. To validate the proposed technique, we conducted experiments using the Million Songs Dataset (MSD) within the LibRec 2.0.0 framework, offering a comprehensive analysis of its effectiveness. Comparative evaluations against recent state-of-the-art recommendation techniques, including GBPR, EALS, and User-Time K-NN, reveal that our approach consistently outperforms alternative methods in terms of Precision, Recall, and F-measure metrics, with performance improvements ranging from 0.5% to 5%. These findings underscore the approach's robustness in handling cold start and data sparsity challenges and its scalability potential for large-scale recommendation applications. This work presents a significant advancement in recommendation system methodologies, demonstrating the feasibility of combining clustering and ARM to enhance collaborative filtering techniques in diverse, sparse environments.</p>

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CFCAI: improving collaborative filtering for solving cold start issues with clustering technique in the recommender systems

  • 期刊论文

  • 发表日期：

    2025年1月20日

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  • Navid Khaledian,
  • Amin Nazari,
  • Masoud Barkhan

摘要

Recommendation systems are crucial in managing data overload, enabling online platforms to provide users with personalized recommendations. However, these systems often encounter significant challenges, such as the cold start problem and data sparsity, which hinder recommendation accuracy. To address these issues effectively, this study proposes an innovative approach that leverages implicit knowledge of users and items, structured into three primary stages. First, we employ clustering techniques to segment the user base, which reduces data volume and mitigates sparsity, enhancing the system's ability to deliver accurate recommendations. In the second phase, Association Rule Mining (ARM) analyses users' implicit interaction records, allowing us to derive valuable association rules and better understand user preferences. Finally, in the third stage, the system leverages these insights to suggest optimal items to each user, enhancing personalization. To validate the proposed technique, we conducted experiments using the Million Songs Dataset (MSD) within the LibRec 2.0.0 framework, offering a comprehensive analysis of its effectiveness. Comparative evaluations against recent state-of-the-art recommendation techniques, including GBPR, EALS, and User-Time K-NN, reveal that our approach consistently outperforms alternative methods in terms of Precision, Recall, and F-measure metrics, with performance improvements ranging from 0.5% to 5%. These findings underscore the approach's robustness in handling cold start and data sparsity challenges and its scalability potential for large-scale recommendation applications. This work presents a significant advancement in recommendation system methodologies, demonstrating the feasibility of combining clustering and ARM to enhance collaborative filtering techniques in diverse, sparse environments.