A Multi-Criteria Recommendation Systems Based on Behavioral Tendencies: BADPM Model

Öz

Although contemporary recommender systems achieve highly effective recommendation performance, they continue to suffer from popularity bias—a pervasive issue that restricts users’ exposure to less popular yet potentially relevant items. This study develops a mechanism that addresses popularity bias in multi-criteria recommendation systems, increasing satisfaction and including unpopular products in the list. The proposed method is based on the tendency for individuals to prioritize the preferences of users who are similar to their own tastes and preferences in their decision-making processes, while only slightly considering the evaluations of users with opposing tendencies. Furthermore, it is assumed that products negatively evaluated by users with opposing tendencies can sometimes become more attractive to the target user. Building on this behavioral foundation, the approach considers both the preferences of similar users and the evaluations of users with opposing tendencies when generating a recommendation list. Comparative analyses of the proposed method with traditional methods are conducted on three different datasets, and the results demonstrate that the proposed approach is successful in reducing popularity bias and improving recommendation quality.

Anahtar Kelimeler

• Multi-criteria recommender systems
• Popularity bias
• Personalized recommendations
• Behavioral modeling

Davranışsal Eğilimlere Dayalı Çok Ölçütlü Öneri Sistemleri: BADPM Modeli

Öz

Günümüzde öneri sistemleri oldukça başarılı tavsiyeler üretse de kullanıcıların daha az bilinen ancak potansiyel olarak ilgi çekici ürünlere erişimini kısıtlayan önemli bir sorun olan popülerlik yanlılığı ile karşı karşıya kalmaktadır. Bu çalışmada çok ölçütlü öneri sistemlerinde popülerlik yanlılığı probleminin ele alındığı, memnuniyetin arttırılıp buna ek olarak listede popüler olmayan ürünlerin yer aldığı bir mekanizma geliştirilmiştir. Önerilen yöntemde, bireylerin karar verme süreçlerinde kendi zevk ve eğilimlerine benzeyen kullanıcıların tercihlerine daha fazla önem vermesine, buna karşılık zıt eğilimlere sahip kullanıcıların değerlendirmelerini sınırlı düzeyde dikkate alma eğilimine dayanmaktadır. Dahası, zıt eğilimli kullanıcıların olumsuz değerlendirdiği ürünlerin kimi durumlarda hedef kullanıcı için daha çekici hâle gelebileceği varsayılmaktadır. Bu davranışsal temelden hareketle geliştirilen yaklaşım, öneri listesi oluştururken hem benzer kullanıcıların tercihlerini hem de zıt eğilimlere sahip kullanıcıların değerlendirmelerini birlikte ele almaktadır. Üç farklı veri seti üzerinde önerilen yöntem ile klasik yöntemlerin karşılaştırmalı analiz sonuçları yapılmış ve elde edilen sonuçlar, önerilen yaklaşımın popülerlik yanlılığını azaltmada ve öneri kalitesini artırmada başarılı olduğunu göstermiştir.

Anahtar Kelimeler

• Çok ölçütlü öneri sistemleri
• Popülerlik yanlılığı
• Kişiselleştirilmiş öneriler
• Davranışsal modelleme

Kaynakça

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