İşbirlikçi Filtreleme Algoritmalarının Çok-Beğenilen Ürünlere Yönelik Yanlılığı

Abstract

Öneri sistemleri, bireysel kullanıcılara herhangi bir kişisel çaba gerektirmeden geçmişteki tercihlerine ve özelliklerine göre uygun ürünleri/hizmetleri öneren otomatikleştirilmiş araçlardır. Bu sistemlerde, işbirlikçi filtreleme algoritmaları, ürünler için bireysel tahminler veya kullanıcılar için tercih edilir ürünlerin sıralı bir listesini üretmek için en çok kullanılan yaklaşımlardır. Bu tür algoritmaların verimliliği genellikle sağlanan önerilerin doğruluğu ile değerlendirilse de, ürün kataloğu kapsamı gibi doğruluk-üstü değerlendirmeler de nitelikli önerilerde kritik faktörler olarak kabul edilir. Ancak, son zamanlarda yapılan birçok çalışma, bu algoritmaların, belirli özellikleri (örn. popülerlik) nedeniyle bazı ürünleri üretilen sıralı listelerde diğerlerinden daha çok öne çıkarma eğiliminde olduğunu göstermiştir. Bu çalışmada, ürün profillerini farklı bir bakış açısıyla, beğenilme dereceleriyle irdeliyor ve işbirlikçi filtreleme algoritmalarının çok beğenilen ürünlere yönelik bir yanlılığının olup olmadığını araştırıyoruz. Bu amaçla, üç farklı kategoriden dokuz önemli işbirlikçi filtreleme algoritmasını kullanıyoruz ve iki gerçek-dünya veri kümesi üzerinde çeşitli deneyler gerçekleştiriyoruz. Deneysel sonuçlar, hemen hemen tüm algoritmaların çok beğenilen ürünlere yönelik güçlü bir yanlılığının olduğunu ve SVD ile SVD++ gibi matris çarpanlarına ayırma tabanlı algoritmaların yüksek kalitede öneriler üretmede diğerlerinden daha başarılı olduğunu göstermiştir.

Keywords

• Öneri Sistemleri
• İşbirlikçi Filtreleme
• Algoritmik Yanlılık
• Ürün Profili
• Katalog Kapsamı

Collaborative Filtering Algorithms’ Bias Towards Highly-liked Items

Abstract

Recommender systems are automated tools that suggest appropriate products/services to individual users based on their preferences in the past and characteristics without requiring any personal effort. In these systems, collaborative filtering algorithms are the most utilized approaches to produce individual predictions or a ranked list of preferable items for users. Although such algorithms' efficiency is generally assessed with the accuracy of provided recommendations, beyond-accuracy evaluations such as item catalog coverage are also considered critical factors in qualified recommendations. However, many recent studies demonstrate that these algorithms tend to feature certain items than others in the produced ranked lists because of their specific properties (e.g., popularity). In this study, we scrutinize item profiles with a different point of view, the degrees of being liked, and investigate whether there is any bias of collaborative filtering algorithms towards highly-liked items or not. To this end, we adopt nine prominent collaborative filtering algorithms in three different categories and perform various experiments on two real-world datasets. The experimental results demonstrate that almost all algorithms are strongly biased towards highly-liked items, and matrix factorization based algorithms such as SVD and SVD++ are more successful than others in producing high-quality recommendations.

Keywords

• Recommender Systems
• Collaborative Filtering
• Algorithmic Bias
• Item Profile
• Catalog Coverage

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