DAILY CONSUMER PURCHASE SUGGESTED LIST BY RECOMMENDED SYSTEM / SANAA SAEED MUSALLAM AL RAWAHI; SUPERVISOR: PROF. DR. ERBUĞ ÇELEBİ

Yazar: Katkıda bulunan(lar):Dil: İngilizce 2022Tanım: 78 sheets; 31 cm. Includes CDİçerik türü:
  • text
Ortam türü:
  • unmediated
Taşıyıcı türü:
  • volume
Konu(lar): Tez notu: Thesis (MSc) Cyprus International University. Institute of Graduate Studies and Research Information Technologies Department Özet: ABSTRACT The aim of this study is to use algorithms to build a recommender system that can generate a suggestion for consumer shopping list. We analyzed the effectiveness of a recommender system using three different methods collaborative filtering (CF), Content-based filtering (C-B) and Hybrid). We investigate and test each approach of the recommended system. Next we have evaluated the accuracy of the system using Recall@5 scored 79% for popularity and 67% for CF. Recall@10 scored for popularity and CF 91% and 79% respectively, Precision@5 scored 77% and 75% for Hybrid and CF approaches Precision@10 scored 88% for Hybrid and 82% for CF. The harmonic evaluation for both Recall and Precision F1@10 scored 88% and 80% for CF and Hybrid approach. Finally, we observe that the content-based scored the lowest which is we conclude that it is superior to CF for typical consumer purchases. We observed that hybrid approach provides the best results for making recommendations to brand-new users with no prior information. Keywords: Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, Precision, Recall, Recommendation System
Materyal türü: Thesis
Mevcut
Materyal türü Geçerli Kütüphane Koleksiyon Yer Numarası Durum Notlar İade tarihi Barkod Materyal Ayırtmaları
Thesis Thesis CIU LIBRARY Tez Koleksiyonu Tez Koleksiyonu YL 2679 R29 2022 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Inormation Technologies Department T3008
Suppl. CD Suppl. CD CIU LIBRARY Görsel İşitsel YL 2679 R29 2022 (Rafa gözat(Aşağıda açılır)) Kullanılabilir Inormation Technologies Department CDT3008
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Includes bibliography (sheets 75-78)

Thesis (MSc) Cyprus International University. Institute of Graduate Studies and Research Information Technologies Department

Includes bibliography (sheets 75-78)

ABSTRACT
The aim of this study is to use algorithms to build a recommender
system that can generate a suggestion for consumer shopping list.
We analyzed the effectiveness of a recommender system using three
different methods collaborative filtering (CF), Content-based
filtering (C-B) and Hybrid). We investigate and test each approach
of the recommended system. Next we have evaluated the accuracy
of the system using Recall@5 scored 79% for popularity and 67%
for CF. Recall@10 scored for popularity and CF 91% and 79%
respectively, Precision@5 scored 77% and 75% for Hybrid and CF
approaches Precision@10 scored 88% for Hybrid and 82% for CF.
The harmonic evaluation for both Recall and Precision F1@10
scored 88% and 80% for CF and Hybrid approach. Finally, we
observe that the content-based scored the lowest which is we
conclude that it is superior to CF for typical consumer purchases.
We observed that hybrid approach provides the best results for
making recommendations to brand-new users with no prior
information.
Keywords: Collaborative Filtering, Content-Based Filtering, Hybrid
Filtering, Precision, Recall, Recommendation System

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