Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering
Yazarlar (1)
Dr. Öğr. Üyesi Burcu DEMİRELLİ OKKALIOĞLU Yalova Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(Ulusal alan endekslerinde (TR Dizin, ULAKBİM) yayınlanan tam makale)
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| Dergi Adı | Sakarya University Journal of Computer and Information Sciences (Online) | ||
| Dergi ISSN | 2636-8129 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2021 |
| Kabul Tarihi | 19-09-2021 | Yayınlanma Tarihi | 31-12-2021 |
| Cilt / Sayı / Sayfa | 4 / 3 / 287–301 | DOI | 10.35377/saucis...953348 |
| Makale Linki | http://dx.doi.org/10.35377/saucis...953348 | ||
| UAK Araştırma Alanları |
Bilgisayar Bilimleri ve Mühendisliği
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| Özet |
| Recommender systems offer tailored recommendations by employing various algorithms, and collaborative filtering is one of the well-known and commonly used of those. A traditional collaborative filtering system allows users to rate on a single criterion. However, a single criterion may be insufficient to indicate preferences in domains such as restaurants, movies, or tourism. Multi-criteria collaborative filtering provides a multi-dimensional rating option. In similarity-based multi-criteria collaborative filtering schemes, existing similarity methods utilize co-users or co-items regardless of how many there are. However, a high correlation with a few co-ratings does not always provide a reliable neighborhood. Therefore, it is very common, in both single- and multi-criteria collaborative filtering, to weight similarities with functions utilizing the number of co-ratings. Since multi-criteria collaborative filtering is yet growing, it lacks a comprehensive view of the effects of similarity weighting. This work studies multi-criteria collaborative filtering and the literature of binary vector similarities, which are frequently used for weighting, by giving a related taxonomy and conducts extensive experiments to analyze the effects of weighting similarities on item- and user-based multi-criteria collaborative filtering. Experimental findings suggest that prediction accuracy of item-based multi-criteria collaborative filtering can be boosted by especially binary vector similarity measures which do not consider mutual absences. |
| Anahtar Kelimeler |
| collaborative filtering | multi-criteria | similarity-weighting | binary vector similarity |
BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 1 |