Distributed Recommender Systems with Sentiment Analysis

Bu çalışmanın amacı kullanıcı puanlama temelli tavsiye sistemlerinin, kullanıcı puanları yerine duygu analizinden elde edilen değerler ile büyük veri üzerinden gerçeklenmesidir. Internet üzerinden e-ticaret sistemlerinin yaygınlaşması ile çok fazla kullanıcı verisi oluşması, alışılmış depolama sistemlerinin artık yeterli gelmemesi ve verinin bölünmesi durumunu oluşturmuştur. Ancak dağıtık dosya sistemleri teknolojileri ile veri bütünlüğünü sağlamak mümkündür. Bu veri üzerinden makine öğrenmesi algoritmalarının çalıştırılması ve sonuçların değerlendirilmesine büyük ihtiyaç duyulmaktadır. Bu çalışmada tavsiye sistemlerinin dağıtık veri üzerinden değerlendirilmesinde, doğal dil işleme adımlarının sisteme eklenmesi ile sağlanan iyileştirme raporlanmıştır.

Büyük Veride Tavsiye Sistemlerini Duygu Analizi ile Desteklemek

The aim of this study is to realize a recommender system (RS) for big data application by using sentiment analysis instead of user ratings. By becoming widespread of e-commerce systems through internet, too much user data has became available. So traditional storage systems remained incapable and the stored data is divided. Nowadays we collect lots of reviews from users and feedback on e-commerce web sites therefore the importance of increasing big data analysis technology, which increases the need of big calculation. In this study, we report the performance improvement by adding the natural language processing steps to the classical recommender system.

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