Quality Analysis of Shopee Seller Portal by Using Category Recommendation System Approach

Abstract

Consumers have shifted away from traditional transactions to online shopping as a result of the COVID-19 pandemic, digital social life, and staying at home. This has a significant impact on the volume of transactions for several ecommerce platforms, including Shopee, which must continue to improve customer satisfaction. One way to accomplish this is to improve the accuracy of the automatic category recommendation, which currently contains a significant number of errors made by the seller. The Shopee Data Scraper was used to collect data, and two qualitative analyses, namely content and thematic analysis, were used to determine how many instances of automatic category recommendation fraud were committed by the seller.According to the content analysis, there is a 29 percent error in product category selection. Meanwhile, 75.1 percent of products should be classified separately but are instead classified as ‘others.’ Following that, 72.7 percent of product titles contain the same words as existing categories.From this research, we managed to get an analysis of the percentage of errors in the automatic category recommendation mechanism from the Shopee platform which causes sellers to place their products in the wrong category so that they can be used for suggestions for improvement or further research.