A Novel Hybrid Item-Based Similarity Method to Mitigate the Effects of Data Sparsity in Multi-Criteria Collaborative Filtering
Yazarlar (1)
Dr. Öğr. Üyesi Burcu DEMİRELLİ OKKALIOĞLU Yalova Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q2) | ||
| Dergi ISSN | 2169-3536 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 01-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-01-2025 |
| Cilt / Sayı / Sayfa | 13 / 1 / 64660–64686 | DOI | 10.1109/ACCESS.2025.3559398 |
| Makale Linki | https://doi.org/10.1109/access.2025.3559398 | ||
| UAK Araştırma Alanları |
Bilgisayar Bilimleri ve Mühendisliği
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| Özet |
| Data sparsity presents a significant challenge for Recommendation Systems, particularly in neighborhood-based approaches that rely on co-ratings to compute similarity. As co-ratings decrease, these methods often struggle to generate accurate recommendations. Addressing the persistent challenge of data sparsity, a novel similarity method, hybrid item-based, for multi-criteria collaborative filtering is proposed in this study. The proposed approach introduces a local similarity measure that combines a modified Jaccard similarity with a distance-based method, enhancing item-to-item similarity calculations. Additionally, it incorporates -divergence as a global similarity measure, leveraging the probability density distribution of ratings to compute similarities even when user overlap is minimal or absent. This dual approach allows our method to produce reliable similarity measures in sparse data environments. The … |
| Anahtar Kelimeler |
| Collaborative filtering | item-based | multi-criteria | probability density distribution | similarity measure | sparsity |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 1 |
| Google Scholar | 3 |