Öneri Sistemlerinde Güncel Eğilimler: Yaklaşımlar ve Gelecek Yönelimler Üzerine Bir Araştırma

Year 2025, Volume: 10 Issue: 1, 53 - 91, 01.06.2025

Berke Akkaya

Abstract

Bu makale, dijital ortamlarda kullanıcı deneyimini ve bilgi erişimini geliştirmede tavsiye sistemlerinin artan önemini tartışmaktadır. Veri seyrekliği, soğuk başlangıç sorunu ve ölçeklenebilirlik gibi zorlukları tanımlayarak gelişmiş makine öğrenimi tekniklerine duyulan ihtiyacı vurgulamaktadır. İşbirlikçi filtreleme, içerik tabanlı filtreleme ve hibrit yaklaşımlar dahil olmak üzere çeşitli metodolojiler incelenmektedir. Veri seyrekliği ve karmaşık veri ilişkilerini ele almak için grafik tabanlı işbirlikçi filtreleme, grafik sinir ağları ve derin öğrenme gibi yenilikler vurgulanmaktadır. Çalışmada ayrıca soğuk başlangıç sorununu çözmek ve değişen kullanıcı tercihlerine uyum sağlamak için dikkat mekanizmaları ve sıralı modelleme üzerinde durulmaktadır. Kullanıcı güveni ve şeffaflık oluşturmak için açıklanabilir yapay zekanın önemi vurgulanmaktadır. İleriye dönük olarak, makale, kişiselleştirme ve alaka düzeyini artırmak için alanlar arası öneri modellerinde ve çeşitli veri kaynaklarının entegrasyonunda ilerlemeler öngörmektedir. Genel olarak, zorlukların üstesinden gelmek ve dijital platformlarda kullanıcı memnuniyetini artırmak için sofistike metodolojileri savunmakta ve tavsiye teknolojilerinin geleceğinde inovasyonun rolünün altını çizmektedir.

Keywords

Öneri Sistemleri, İşbirlikçi Filtreleme, İçerik Tabanlı Filtreleme, Hibrit Öneri Yaklaşımları

Current Trends in Recommender Systems: A Survey of Approaches and Future Directions

Abstract

This paper discusses the growing importance of recommender systems in enhancing user experience and information access in digital environments. It identifies challenges such as data sparsity, the cold-start problem, and scalability, emphasizing the need for advanced machine learning techniques. Various methodologies are explored, including collaborative filtering, content-based filtering, and hybrid approaches. Innovations like graph-based collaborative filtering, graph neural networks, and deep learning are highlighted for addressing data sparsity and complex data relationships. The paper also emphasizes attention mechanisms and sequential modeling to resolve the cold-start problem and adapt to changing user preferences. It stresses the significance of explainable AI for building user trust and transparency. Looking ahead, the paper anticipates advancements in cross-domain recommendation models and the integration of diverse data sources to enhance personalization and relevance. Overall, it advocates for sophisticated methodologies to overcome challenges and improve user satisfaction in digital platforms, underscoring the role of innovation in the future of recommendation technologies

Keywords

Recommender Systems, Collaborative Filtering, Content-based Filtering, Hybrid Recommendation Approaches

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