Bir İçerik-Tabanlı Ürün Öneri Yaklaşımı

Year 2022, , 119 - 128, 29.03.2022

Yıltan Bitirim

https://doi.org/10.21605/cukurovaumfd.1094997

Abstract

Bu çalışmada, bir içerik-tabanlı öneri yaklaşımı önerilmektedir. Bu yaklaşım, IMDB’nin ön işlenmiş 245 en iyi film özetlerini ve anket yöntemiyle ortaya çıkan kullanıcının favori film türlerini kullanmakta ve daha sonra, -bir ürün havuzundan- kullanıcının “beğenebileceği” potansiyelde ürünleri önermektedir. Test için; Amazon.com’dan gerçek ürünleri içeren bir test veri seti yaratılmıştır, ve önerilen yaklaşımı kullanan ve kullanıcıları bu yaklaşımın sonuçlarını değerlendirmek için yönlendiren bir Web uygulaması tasarlanıp geliştirilmiştir. 52 gönüllü denek teste katılmıştır. Denek, görüntülenen 10 ürünün her birini ayrı ayrı incelemiş ve derecelendirmiştir. Derecelendirme, “Hiç” (%0), “Biraz” (%25), “Orta” (%50), “Çok” (%75) ve “Son derece” (%100) olarak beş-seviyeli Likert-türü ölçüye dayalı yapılmıştır. Deneklerin ürünleri orta derecede beğendiğini söylemek mümkündür. Ürün değerlendirmeleri “beğenilen” ve “beğenilmeyen” olarak iki kategoride kategorize edildiğinde, deneklerin ürünlerin yaklaşık %78,65’ini beğendiğini söylemek mümkündür. Bu yaklaşım, kullanıcının “beğenebileceği” potansiyelde ürünler önermek için Amazon.com gibi e-ticaret uygulamalarına entegre olabilir.

Keywords

Öneri sistemleri, İçerik-tabanlı, Metin madenciliği, Bilgi filterleme, Bilgi erişim, E-ticaret

A Content Based Product Recommendation Approach

Abstract

In this study, a content-based recommendation approach is proposed. It utilizes the preprocessed 245 top movie summaries of IMDB and the favorite movie genres of the user elicited with the questionnaire method and then, recommends potential products -from a product pool- that the user can “like”. For testing; a test dataset that consists of real products from Amazon.com was created, and a Web application that uses the proposed approach and leads the users to evaluate the results of this approach was designed and developed. 52 volunteered subjects attended the test. The subject examined and graded each of the 10 products displayed. Grading was based on the five-level Likert-type scale “Not at all” (0%), “Slightly” (25%), “Moderate” (50%), “Very” (75%), and “Extremely” (100%). It is possible to say that the subjects are moderately liked the products. When the product evaluations are categorized in two categories as “liked” and “disliked”, it is possible to say that the subjects liked ~78.65% of the products. This approach could be integrated into e-commerce applications like Amazon.com for recommending potential products that the user can “like”.

Keywords

Recommendation systems, Content-based, Text mining, Information filtering, Information retrieval, E-commerce

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