Dynamic Recommendation System Using Enhanced K-means Clustering Algorithm for E-commerce

Ankush Saklecha, Jagdish Raikwal


E-commerce organizations are growing day by day over time in terms of both business and data. Maximum organizations rely on these e-commerce websites to attract new customers and maintain existing ones. Dynamic recommendation system can be used to achieve this goal. It works towards improving the result factors of product priority displayed over the users search records. . This paper focuses on providing real-time dynamic recommendations to all registered users of the website. Here the dynamic recommendation technology is proposed, which uses enhanced K-Means Algorithm to generate item recommendation. By compiling the real time e-commerce data and comparing the system with existing K-means algorithm, the effectiveness of the proposed system is evaluated. The results prove that the proposed system provides good quality, accuracy and reduces the limitations of the conventional recommendation system. The experimental evaluation is measured on precision, recall and accuracy for proving the robustness of the system.


Data Mining; Clustering; K-means Clustering; Recommendation; E-commerce;

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DOI: https://doi.org/10.26483/ijarcs.v8i5.3248


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