KİŞİSELLEŞTİRİLMİŞ ÜRÜN ÖNERİ SİSTEMİ İÇİN KULLANICI BAZLI İŞBİRLİKÇİ FİLTRELEME VE KÜMELEME KULLANAN HİBRİT BİR YAKLAŞIM

Öz

Günümüz rekabet koşulları firmaları, özellikle perakende ve e-ticaret firmalarını, müşterilerini daha iyi tanımaya, onların tercihlerini ve davranışlarını anlamaya, ihtiyaçlarını tahmin etmeye; böylelikle, onlara kendilerini özel hissettirecek, teklifler sunmaya zorlamaktadır. Firmaların söz konusu kişiselleştirme ihtiyaçlarını karşılayabilmeleri adına kullandıkları yöntemlerden biri ürün öneri sistemleridir. Amaç: Çalışmada, kişiselleştirilmiş ürün öneri sistemleri için literatürde ve iş dünyasında sıklıkla kullanılan yöntemlerden biri olan kullanıcı bazlı işbirlikçi filtreleme yöntemini iyileştirmek adına k-means ile kullanıcı bazlı işbirlikçi filtreleme algoritmalarını birlikte kullanan hibrit bir yaklaşım önerilmesi amaçlanmıştır. Yöntem: Kullanıcı bazlı işbirlikçi filtreleme ve k-means yöntemleri kullanılmıştır. Bulgular: Mevcut yöntem ve önerilen yöntem iki farklı veri seti için uygulanmıştır. Yöntemlerin karşılaştırılması amacıyla veri setleri %80’i eğitim, %20’si de test seti olmak üzere ikiye ayırılmış ve eğitim verisi üzerinden kurulan modellerin test verisindeki hataları (RMSE) hesaplanmıştır. Yapılan karşılaştırma sonucunda her iki veri setinde de önerilen yönteme ilişkin hata değeri daha az olduğu görülmüştür. Özgünlük: Bu çalışma ile sadece kullanıcı-ürün skorları üzerinden çalışan kullanıcı bazlı işbirlikçi filtreleme yöntemine kullanıcılara ilişkin farklı bilgileri de kullanabilen bir yaklaşım önerilmiştir. Ayrıca, önerilen yöntem literatürde sıklıkla kullanılan MovieLens veri setinden uygulanmasının yanı sıra gerçek bir süpermarket verisinde de uygulanmıştır.

Anahtar Kelimeler

• Öneri Sistemleri
• Kümeleme
• Kullanıcı Bazlı İşbirlikçi Filtreleme
• K-Means

A HYBRID APPROACH USİNG USER-BASED COLLABORATIVE FILTERING AND CLUSTERING FOR PERSONALIZED PRODUCT RECOMMENDATION SYSTEM

Öz

Nowadays competition conditions are forced companies, especially retail and e-commerce companies, to know their customers better, to understand their preferences and behaviours, to predict their needs, in this way, to make offers that feel them special. One of the methods used by companies to meet their personalization needs is product recommendation systems. Purpose: In the study, it is aimed to propose a hybrid approach that uses k-means and user-based collaborative filtering algorithms together to improve the user-based collaborative filtering method, which is one of the most frequently used methods in the literature and business world for personalized product recommendation systems. Method: User-based collaborative filtering and k-means methods are used. Findings: The current method and the proposed method were applied for two different data sets. In order to compare the methods, the data sets were divided into two as 80% training and 20% test set, and the errors (RMSE) of the models built on the training data were calculated. As a result of the comparison, it was seen that the error value for the proposed method was less in both data sets. Originality: In this study, an approach that can also use different information about users is proposed to the user-based collaborative filtering method, which works only on user-product scores. In addition, the proposed method has been applied to a real supermarket data as well as being applied from the MovieLens dataset, which is frequently used in the literature.

Anahtar Kelimeler

• Recommendation Systems
• Clustering
• User-Based Collaborative Filtering
• K-Means

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