Kullanıcı ve Öğe Bazlı, Geniş ve Derin Öğrenme Tabanlı Seyahat Öneri Sistemi

Year 2023, Issue: 51, 334 - 351, 31.08.2023

Alihan Öz , Meryem Uzun-per , Mert Bal

https://doi.org/10.31590/ejosat.1296379

Abstract

Teknolojinin gelişmesi ile birlikte artan dijital bilgi miktarı ve internetin yaygınlaşması ile internet üzerinden ürün, hizmet, abonelik gibi ticaret işlemlerinin gerçekleştiği web sitelerinin sayısının da artması, beraberinde, müşterilere kişiselleştirilmiş ve doğru; ürün, hizmet ve abonelikleri sunmanın (önermenin) de önemini artmıştır. Müşterilere önerilerde yaygın olarak kullanılan ürün bazlı, kullanıcı tabanlı ve bu ikisinin birlikte kullanıldığı hibrit geleneksel yaklaşımlar çoğu çalışmada kullanılmaktadır. Geleneksel yaklaşımların, büyük ve seyrek veriler ile çalışma, kullanıcı ve ürün arasındaki karışık ilişkileri bulamama ve soğuk başlangıç (cold start) gibi problemlerinin üstesinden gelmek, derin ve geniş öğrenme sistemlerinin kullanımı ile mümkün olmuştur.
Bu çalışma kapsamında, derin ve geniş sinir ağlarına ve bunların seyahat öneri sistemlerindeki uygulamalarına kapsamlı bir bakış açısı sunulmuştur. Derin öğrenme ile ilgili temel bilgilere yer verildikten sonra, en popüler öneri algoritmaları olan Google'ın Geniş ve Derin Algoritması ve Facebook'un Deep Learning Recommendation Model (DLRM) algoritmasına yer verilmiştir.
Bu çalışma kapsamında, geniş ve derin öğrenme yaklaşımı ile kullanıcı ve ürün özelliklerinin kategorik olanlarının gömme işlemi uygulanarak, nümerik veriler ile modele beslendiği yeni bir seyahat öneri sistemi oluşturulmuştur. Önerilen yöntem gerçek bir seyahat acentesi şirketinin veri seti üzerinde uygulanmıştır. Bu çalışma sonucunda kullanıcılara verilen en iyi beş öneride, %82.37 doğruluk oranı yakalanmıştır.

Keywords

öneri sistemleri, derin öğrenme, derin ve geniş öneri sistemleri, içerik tabanlı, işbirlikçi, hibrit, seyahat öneri sistemleri

Thanks

Bu çalışmada veri sağlayıcısı olan BiletBank'a desteklerinden dolayı teşekkür ederiz.

A User and Item-Based, Wide and Deep Learning Based Travel Recommendation System

Abstract

With the development of technology, the increasing amount of digital information and the widespread use of the internet, and the increase in the number of websites where trade transactions such as products, services and subscriptions are carried out on the internet, along with it, personalized and accurate; The importance of recommending products, services and subscriptions has also increased. Product-based, user-based, and hybrid traditional approaches, which are widely used in recommendations to customers, are used in most studies. Overcoming the problems of traditional approaches such as working with large and sparse data, inability to find complex relationships between user and product, and cold start has been possible with the use of deep and wide learning systems.
Within the scope of this study, a comprehensive view of deep and wide neural networks and their applications in travel recommendation systems is presented. After giving the basic information about deep learning, Google's Wide and Deep Algorithm and Facebook's Deep Learning Recommendation Model (DLRM) algorithm, which are the most popular recommendation algorithms, are included.
Within the scope of this study, a new travel recommendation system was created in which numerical data is fed to the model by applying the embedding process of categorical user and product features with a broad and deep learning approach. The proposed method was applied on the data set of a real travel agency company. As a result of this study, 82.37% accuracy rate was achieved in the top five recommendations given to the users.

Keywords

recommendation systems, deep learning, deep and wide recommendation systems, content-based, collaborative, hybrid, travel recommendation systems.

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