Product Label Clustering Based on Search Keywords for Product Name Recommendations in Ecommerce
Bryan, Daniel Alexander, Maria Susan Anggreainy, Devi Fitrianah, Ajeng Wulandari · 2024
One of the key challenges in e-commerce is how to provide relevant and personalized product recommendations to users. To achieve this, data analysis and text processing techniques are essential. This research aims to analyze product descriptions based on purchase history and product labels on e-commerce platforms. The objectives of this study are: Analyzing and clustering product descriptions using clustering techniques such as K-Means Clustering. This helps in understanding the patterns and relationships between the various product descriptions present. Using the results from clustering to develop an effective recommendation engine. This recommendation engine will utilize collaborative filtering techniques, where product recommendations are suggested based on similarities between the user's purchase history and relevant product clusters. By analyzing data and developing a recommendation engine based on product descriptions, it is expected to improve the relevance and accuracy of product recommendations for users. This will not only improve the user's shopping experience but can also increase sales and customer satisfaction on e-commerce platforms.