TY  - JOUR
T1  - Enhancing Service Excellence: Blockchain-AI TF-IDF Recommendations
AU  - Dini, Mohamed Abubakar 
AU  - Kim, Dong-Seong 
AU  - Jun, Taesoo 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2024
DA  - 2024/1/1
DO  - 10.7840/kics.2024.49.9.1274
KW  - Algorithm
KW  - Artificial Intelligence
KW  - Blockchain
KW  - Cosine Similarity
KW  - Data
KW  - KNN
KW  - Recommendation system
KW  - Software
KW  - TD-IDF
AB  - 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.