Tavsiye Sistemlerinde Derin Otokodlayicilar: Nesnelerin Interneti Hizmet Tavsiyesi Üzerine Bir Uygulama

Year 2021, Volume: 14 Issue: 3, 267 - 277, 31.07.2021

Sule Birim

https://doi.org/10.17671/gazibtd.685500

Abstract

Nesnelerin İnterneti (IoT) küçük ve birbirine bağlı cihazlardan oluşan ve internet aracılığıyla bilgi paylaşımı sağlayan cihazlardan oluşan bir sistemdir. Gelecek IoT cihazlarının gerçekleştirdiği hizmetlerin sayısının oldukça artmasını beklemektedir. Bu nedenle IoT hizmeti önerme, gelecekte çok önemli bir faaliyet halini alacaktır. Bu çalışma, IoT hizmetleri tavsiyesi için en iyi yöntemi bulmayı hedeflemektedir. Bu amaç doğrultusunda bu çalışma derin otokodlayıcılar yöntemini, kullanıcılara IoT hizmet ve uygulamalarını kullandıkları cihazlara bağlı olarak önermek için kullanmayı hedeflemektedir. Derin otokodlayıcılar, kullanıcı hizmet tercih matrisini tahmin edebilmek için sinir ağlarını kullanır. Bu çalışmada kullanılan veri, gerçek bir Sosyal IoT veri setinden yararlanılarak oluşturulmuştur. Sonuçlara göre derin otokodlayıcılar, teknoloji harikası öneri yöntemlerine göre daha başarılı bir performans ortaya koymuştur. Bu sonuca ek olarak özel bir çeşit aktivasyon fonksiyonu ile oto kodlayıcıların performansının arttırılabileceği görülmüştür. Bu çalışma IoT hizmet önerisi literatürüne geleneksel ve teknoloji harikası kabul edilen yöntemlere bir alternatif sunarak katkı sağlamaktadır.

Keywords

Nesnelerin İnterneti (IoT), Öneri sistemleri, Hizmet önerisi, İşbirlikçi Filtreleme, Derin Otokodlayıcılar, Sinir Ağları

Deep AutoEncoders in Recommender Systems: An Application about Internet of Things Service Recommendation

Abstract

The Internet of Things (IoT) is a system that includes small interconnected devices sharing information through the Internet. Future expects an increasing number of services in IoT devices. Therefore, recommending IoT services will be a vital task for the future of IoT and the convenience of the users. This study aims to find the best methodology to provide IoT service recommendation. With this aim, this study proposes deep autoencoders methodology to recommend services and applications to users based on the devices they own. Deep autoencoders utilize neural networks to predict user service preference matrix. The data used in this study is constructed from a real-world Social IoT dataset. The results showed that deep autoencoders outperformed the state-of-the-art recommendation methods. According to the results, Deep autoencoders improved performance indicators varying between 13.5% and 69.5% compared to other methods. Findings also indicate that the performance of the deep autoencoders can be enhanced by using ELU (exponential linear units), a specific type of activation function. This study contributes to the IoT service recommendation literature by proposing a superior approach when compared to the traditional recommendation techniques.

Keywords

The Internet of Things (IoT), Recommendation Systems, Service Recommendation, Collaborative Filtering, Deep AutoEncoders, Neural Networks

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