Konu benzerliğine dayalı makale tavsiye sistemi

Year 2021, , 175 - 183, 20.10.2021

Esra Gündoğan , Mehmet Kaya

https://doi.org/10.53070/bbd.990438

Abstract

Akademik ilerleme ile beraber araştırmacılar tarafından yayınlanan makale sayısı her geçen gün artmaktadır. Yayın sayısındaki artış ilgilenilen konu ile ilgili çalışmalara ulaşmayı zorlaştırmaktadır. Tavsiye sistemleri bu noktada araştırmacılar için önemli bir araçtır. Kullanıcıların profiline ya da yayınların konu benzerliğine dayalı makale tavsiye sistemleri istenilen bilgiye ulaşmada kullanıcılara oldukça yardımcı olmaktadır. Bu çalışmada girilen makalenin konusuna benzer makaleleri tavsiye etmek için bir yaklaşım önerilmiştir. Oluşturulan sistem doküman benzerliği, kümeleme ve anahtar kelime çıkarımı konularının birleştirilmesiyle hem anahtar kelime hem de içerik benzerliklerini dikkate alarak makale tavsiye etmektedir. Derin öğrenme tabanlı yöntemlerin çalışmanın her adımında kullanılması tavsiye sisteminin performansını artırmıştır. Çalışma bilgisayar biliminde makine öğrenmesi, yapay zeka, insan bilgisayar etkileşimi gibi farklı kategorilerden makaleleri içeren bir veri seti üzerinde uygulanmıştır. Kullanıcılara sorguları ile yüksek benzerliğe sahip makaleler önerilmiştir. Böylece istenilen konuya yönelik çalışmalara erişim daha hızlı ve daha kolay bir hale getirilmiştir.

Keywords

anahtar kelime çıkarımı, doküman benzerliği, kümeleme, makale tavsiye sistemi

Supporting Institution

Fırat Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

MF.20.09

Thanks

Bu çalışma Fırat Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından MF.20.09 numaralı proje kapsamında desteklenmiştir.

Paper recommendation system based on topic similarity

Abstract

Along with academic progress, the number of papers published by researchers is increasing day by day. The increase in the number of publications makes it difficult to reach studies on the subject of interest. Recommendation systems are an important tool for researchers at this point. Paper recommendation systems based on the profile of the users or the topic similarity of the publications are very helpful to the users in reaching the desired information. In this study, an approach is proposed to recommend papers similar to the subject of the paper searched. The created system recommends papers considering both keyword and content similarities by combining document similarity, clustering, and keyword extraction. The use of deep learning-based methods in every step of the study has increased the performance of the recommendation system. The study has been applied to a dataset containing papers from different categories such as machine learning in computer science, artificial intelligence, human-computer interaction. Users are offered papers with high similarity to their queries. Thus, access to studies on the subject of interest has been made faster and easier.

Keywords

keyword extraction, document similarity, clustering, paper recommendation system

Project Number

MF.20.09

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