Content Based VGG16 Image Extraction Recommendation
Dublin Core
Title
Content Based VGG16 Image Extraction Recommendation
Subject
Image Based, Content Based Filtering, Recommendation System, VGG16
Description
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.
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.
Creator
Muhammad Royyan Saputra1
, Arif Dwi Laksito2
, Arif Dwi Laksito2
Publisher
Universitas Amikom Yogyakarta
Date
30-06-2022
Contributor
Fajar Bagus W
Format
PD
Language
Indonesia
Type
Text
Files
Collection
Citation
Muhammad Royyan Saputra1
, Arif Dwi Laksito2, “Content Based VGG16 Image Extraction Recommendation,” Repository Horizon University Indonesia, accessed June 26, 2025, https://repository.horizon.ac.id/items/show/9172.