DEĞİŞİMLİ EN KÜÇÜK KARELER VE KOSİNÜS BENZERLİK TEKNİKLERİ KULLANILARAK YEMEK TAVSİYE SİSTEMİ OLUŞTURMA

Year 2024, , 1 - 17, 06.06.2024

Merve Cengiz , Tuğba Yıldız

https://doi.org/10.53070/bbd.1389078

Abstract

Bu çalışmada, Allrecipes.com web sitesindeki yemek tariflerine ve üyeler tarafından verilen oylara dayalı bir yemek tavsiye sistemi geliştirildi. Toplam 1840 yemek tarifi (Diyabetik - Glutensiz - Ketojenik - Düşük Sodyum - Düşük Kolesterol - Vejetaryen – Vegan) Allrecipes.com'dan web scraping yöntemi ile kazındı ve Python'da analiz edildi. Tavsiye Sistemi, Değişimli En Küçük Kareler (DEKK) yöntemi kullanılarak oluşturuldu. Diyet Yemek Tavsiye Sistemi, kosinüs benzerlik yöntemi kullanılarak gerçekleştirildi. DEKK yönteminin büyük veri ile uygulaması bulut üzerinde gerçekleştirildi. Modelin hata kareler ortalamasının karekökü 0.495 olarak bulundu. Modelin önerdiği yemekler kullanıcı bazlı incelendi ve sonuçların tutarlı olduğu belirlendi. En çok tavsiye edilen yemekler incelendiğinde, vejetaryen tariflerin ilk sırada yer aldığı; toplamda ise ketojenik tariflerin yüksek sayıda önerildiği görüldü. Sonuç olarak, yemek tarifleri aracılığıyla yiyecekler hakkında fikir sahibi olmak ve diyetlerine göre yiyecek seçmek isteyen kullanıcılara doğru öneriler üreten web tabanlı bir yemek öneri sistemi oluşturuldu.

Keywords

Tavsiye sistemi, değişimli en küçük kareler, kosinüs benzerlik, büyük veri

BUILDING FOOD RECOMMENDATION SYSTEMS USING ALTERNATING LEAST SQUARES AND COSINE SIMILARITY TECHNIQUES

Abstract

The aim of this study was to develop a food recommendation system based on the recipes on the Allrecipes.com website and the ratings given by the members. Total 1840 recipes (Diabetic - Gluten Free - Ketogenic - Low Sodium - Low Cholesterol - Vegetarian - Vegan) were scraped from Allrecipes.com and analysed in Python. Recommendation System was performed by using Alternating Least Square (ALS). Diet Food Recommendation System was performed by using cosine similarity technique. The application of ALS technique with big data was performed on the cloud. The root mean squared error of the model was found 0.495. The foods recommended by the model were examined on a user basis and it was determined that the results were consistent. When the most recommended foods were examined, vegetarian recipes were ranked first, and in total, a high number of ketogenic recipes were recommended. Consequently, we created a web-based food recommendation system that generates accurate recommendations to users who want to have ideas about foods via recipes and choose food according to their diet.

Keywords

Recommendation system, alternating least squares, cosine similarity, big data

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