Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16

Abstract

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.