Content Based VGG16 Image Extraction Recommendation

  • Arif Laksito Universitas Amikom Yogyakarta
  • Muhammad Royyan Saputra Universitas Amikom Yogyakarta
Keywords: Image Based, Content-based Filtering, , Recommendation System, VGG16


Data transfer across numerous platforms has increased dramatically due to the enormous number of visitors or users of the present e-commerce platform. With the rise of increasingly massive data, consumers are finding it challenging to obtain the right data. The recommendation engine may be used to make it simpler to find information that is relevant to the user's needs. Clothing, gadgets, autos, furniture, and other e-commerce items rely on product visualization to entice shoppers. There are millions of images in these items. Displaying the information sought by clients based on visual data is a difficult challenge to address. One strategy that is simple to use in a recommendation system is content-based filtering. This approach will eventually make suggestions to consumers based on previously accessible goods or product descriptions. Content-based filtering works by searching for similarities based on the properties of a product item. User interactions with a product will be recorded and analyzed in order to recommend certain similarities to users. Text-based datasets are used in the majority of content-based filtering studies. In this study, however, we attempt to leverage a dataset received from Kaggle in the form of images of futsal shoes. Then, VGG16 architecture is used to extract the image dataset. The top 5 most relevant item rankings are generated by this recommendation method using cosine similarity. In addition, the NDCG (Normalized Discounted Cumulative Gain) approach is used to assess the results of the suggestions. The NDCG was evaluated in ten test scenarios, with an average NDCG value of 0.855, indicating that the system delivers a reasonable performance suggestion.


Download data is not yet available.


S. K. Addagarla and A. Amalanathan, “SS symmetry Approach for a Similar Image Recommender System,” 2020.

A. O. Salau and S. Jain, “Feature Extraction: A Survey of the Types, Techniques, Applications,” 2019 Int. Conf. Signal Process. Commun. ICSC 2019, no. March, pp. 158–164, 2019, doi: 10.1109/ICSC45622.2019.8938371.

M. Omar, K. Ahmad, and M. A. Rizvi, “Content Based Image Retrieval System,” pp. 345–362, 2015, doi: 10.4018/978-1-4666-8853-7.ch017.

Z. S. Younus et al., “Content-based image retrieval using PSO and k-means clustering algorithm,” Arab. J. Geosci., vol. 8, no. 8, pp. 6211–6224, 2015, doi: 10.1007/s12517-014-1584-7.

L. Yu, F. Han, S. Huang, and Y. Luo, “A content-based goods image recommendation system,” Multimed. Tools Appl., vol. 77, no. 4, pp. 4155–4169, 2018, doi: 10.1007/s11042-017-4542-z.

F. Ullah, B. Zhang, and R. U. Khan, “Image-Based Service Recommendation System: A JPEG-Coefficient RFs Approach,” IEEE Access, vol. 8, no. Dl, pp. 3308–3318, 2020, doi: 10.1109/ACCESS.2019.2962315.

A. E. Wijaya and D. Alfian, “Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering,” J. Comput. Bisnis, vol. 12, no. 1, pp. 11–27, 2018.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

R. J. Gunawan, B. Irawan, and C. Setianingsih, “Pengenalan Ekspresi Wajah Berbasis Convolutional Neural Network Dengan Mdel Arsitektur VGG16 Facial Expression Recognition Based On Convolutional Neural Network with VGG16 Architecture Model,” e-Proceeding Eng., vol. 8, no. 5, p. 6442, 2021.

H. Hartatik, B. P. Sejati, A. N. Fitrianto, and W. Widayani, “A Comparison Study of Model Based Collaborative Filtering Using Alternating Least Square and Singular Value Decomposition,” pp. 185–190, 2021.

I. S. Wahyudi, “Big data analytic untuk pembuatan rekomendasi koleksi film personal menggunakan Mlib. Apache Spark,” Berk. Ilmu Perpust. dan Inf., vol. 14, no. 1, p. 11, 2018, doi: 10.22146/bip.32208.

Y. I. Lubis, D. J. Napitupulu, A. S. Dharma, J. S. Sitoluama, and S. Utara, “Implementasi Metode Hybrid Filtering ( Collaborative dan Content-based ) untuk Sistem Rekomendasi Pariwisata Implementation of Hybrid Filtering ( Collaborative and Content-based ) Methods for the Tourism Recommendation System,” pp. 6–8, 2020.

P. Nuankaew, a survey of the state-of-the-art and possible extensions Areas of Presentations. 2016.

R. H. Mondi, A. Wijayanto, and Winarno, “Recommendation System With Content-Based Filtering Method for Culinary Tourism in Mangan Application,” Itsmart, vol. 8, no. 2, pp. 65–72, 2019.

M. J. Pazzani and D. Billsus, “Content-Based Recommendation Systems,” pp. 325–341, 2007.

W. S. Eka Putra, Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS. 5 (2016), doi:10.12962/j23373539.v5i1.15696..

K. Fukushima, S. Miyake, and T. Ito, “Neocognitron : A neural network model for a mechanism of visual pattern recognition,” pp. 1–40, 2018.

R. E. Howard, W. Hubbard, and L. D. Jackel, “Handwritten Digit Recognition with a Back-Propagation Network,” pp. 396–404.

J. Algoritme et al., “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” vol. 1, no. 1, 2020.

E. Tanuwijaya and A. Roseanne, “Modifikasi Arsitektur VGG16 untuk Klasifikasi Citra Digital Rempah-Rempah Indonesia,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 189–196, 2021, doi: 10.30812/matrik.v21i1.1492.

R. Rismiyati and A. Luthfiarta, “VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification,” Telematika, vol. 18, no. 1, p. 37, 2021, doi: 10.31315/telematika.v18i1.4025.

J. Han, M. Kamber, and J. Pei, Data mining concepts and techniques, Third Edit. Waltham: Elsevier Inc, 2012.

R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong, “Diversifying search results,” Proc. 2nd ACM Int. Conf. Web Search Data Mining, WSDM’09, pp. 5–14, 2009, doi: 10.1145/1498759.1498766.

K. Järvelin and J. Kekäläinen, “Cumulated gain-based evaluation of IR techniques,” ACM Trans. Inf. Syst., vol. 20, no. 4, pp. 422–446, 2002, doi: 10.1145/582415.582418.

Y. G. Hapsari, A. T. Wibowo, F. Informatika, U. Telkom, F. Informatika, and U. Telkom, “Analisis Dan Implementasi Sistem Rekomendasi Menggunakan Most-Frequent Item Dan Association Rule Technique Analysis and Implementation Recommender System Using Most- Frequent Item and Association Rule Technique,” e-Proceeding Eng., vol. 2, no. 3, pp. 7757–7764, 2015.

L. Dzumiroh and R. Saptono, “Penerapan Metode Collaborative Filtering Menggunakan Rating Implisit pada Sistem Perekomendasi Pemilihan Film di Rental VCD,” J. Teknol. Inf. ITSmart, vol. 1, no. 2, p. 54, 2016, doi: 10.20961/its.v1i2.590.

How to Cite
Laksito, A., & Saputra, M. R. (2022). Content Based VGG16 Image Extraction Recommendation . Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(3), 370 - 375.
Artikel Teknologi Informasi