Enhancing Service Excellence: Blockchain-AI TF-IDF Recommendations 


Vol. 49,  No. 9, pp. 1274-1283, Sep.  2024
10.7840/kics.2024.49.9.1274


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  Abstract

Recommendation systems, ubiquitous across diverse sectors such as e-commerce, streaming services, and social media, play a pivotal role in tailoring user experiences. However, their application remains underexplored in sectors like dealerships and vehicles, where personalized suggestions can significantly enhance customer engagement and decision-making. Despite their widespread use, limited attention has been directed towards optimizing recommendation systems for the unique dynamics of the dealership and vehicle sectors, presenting an untapped potential for improvement and innovation. Utilizing software, artificial intelligence, and algorithms, our system addresses user complaints by seamlessly integrating AI algorithms and blockchain technology for enhanced security. Leveraging the Term Frequency-Inverse Document Frequency of Records (TF-IDF) vectorization for precision, the system demonstrates remarkable accuracy (99.8%) through cosine similarity (CS) and K-Nearest Neighbors evaluation. Propelled by advanced AI algorithms, it outperforms other blockchain-based recommendation systems, showcasing its potential in dealership and vehicle-related contexts.

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[IEEE Style]

M. A. Dini, D. Kim, T. Jun, "Enhancing Service Excellence: Blockchain-AI TF-IDF Recommendations," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 9, pp. 1274-1283, 2024. DOI: 10.7840/kics.2024.49.9.1274.

[ACM Style]

Mohamed Abubakar Dini, Dong-Seong Kim, and Taesoo Jun. 2024. Enhancing Service Excellence: Blockchain-AI TF-IDF Recommendations. The Journal of Korean Institute of Communications and Information Sciences, 49, 9, (2024), 1274-1283. DOI: 10.7840/kics.2024.49.9.1274.

[KICS Style]

Mohamed Abubakar Dini, Dong-Seong Kim, Taesoo Jun, "Enhancing Service Excellence: Blockchain-AI TF-IDF Recommendations," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 9, pp. 1274-1283, 9. 2024. (https://doi.org/10.7840/kics.2024.49.9.1274)
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eISSN : 2287 - 3880