Öneri Sistemleri
Özet
Öneri sistemleri, çevrimiçi ürün ve hizmetleri kişiselleştirerek kullanıcılara sunmayı ve karar süreçlerini iyileştirmeyi amaçlayan yapay zekâ tabanlı bilgi filtreleme sistemleridir. Bu sistemler temel olarak kişiselleştirilmiş ve kişiselleştirilmemiş yöntemler olarak ikiye ayrılır. Kişiselleştirilmiş yöntemlerden işbirlikçi filtreleme, benzer kullanıcıların geçmiş etkileşimlerine ve beğenilerine odaklanarak bellek tabanlı ya da model tabanlı tekniklerle çalışırken; içerik tabanlı filtreleme ise kullanıcı profilleri ile öğelerin karakteristik özelliklerini eşleştirir. Bu yöntemlerin sınırlılıklarını aşmak için hibrit yaklaşımlar geliştirilmiştir. Sistemlerin başarısı doğrudan ve dolaylı geri bildirimlerin toplanmasına dayanırken; yüksek yatırım maliyetleri, soğuk başlangıç sorunları, değişen kullanıcı davranışları ve mahremiyet endişeleri gibi zorluklar barındırır. Performans ölçümünde MAE, RMSE, Kesinlik, Duyarlılık ve F-ölçütü gibi metrikler kullanılırken, gelecekte sistemlerin Web 3.0 ve sensör teknolojileriyle entegre edilmesi öngörülmektedir.
Recommender systems are artificial intelligence-based information filtering systems that aim to customize online products and services for users and improve decision-making processes. These systems are mainly divided into personalized and non-personalized methods. Among personalized methods, collaborative filtering focuses on the past interactions and preferences of similar users, operating through memory-based or model-based techniques, while content-based filtering matches user profiles with the characteristic features of items. Hybrid approaches have been developed to overcome the limitations of these methods. The success of the systems relies on collecting explicit and implicit feedback, yet they harbor challenges such as high investment costs, cold start problems, changing user behaviors, and privacy concerns. Metrics like MAE, RMSE, precision, recall, and F-measure are used for performance measurement, and in the future, these systems are envisioned to be integrated with Web 3.0 and sensor technologies.
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