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Improvement of collaborative filtering recommendation system to resolve sparsity problem using combination of clustering and opinion mining methods
Krisdhamara W.P.a, Pharmasetiawan B.a, Mutijarsa K.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Online shopping or e-commerce sites are developing quite rapidly along with the development of the internet and technology in Indonesia. However, buyers in e-commerce still have problems trusting when buying products. Feedback from other buyers, either comments or ratings, is considered capable of influencing the behavior of buyers in buying items. In addition, a system of recommendations is known to help the buyer’s decision to choose the product to be purchased. Collaborative Filtering (CF) is one of the popular recommendation methods. However, the ability of CF is limited by the problem of sparsity. In this study, we proposed model-based CF recommendation system that has good accuracy and quality recommendations by combining clustering methods using k-means++ to reduce data dimensions and opinion mining or sentiment analysis using Multinomial Naïve Bayes to filter recommendation results. K-means++ was chosen because it proved to have good quality clustering results, while Multinomial Naïve Bayes was chosen because of its simplicity and good accuracy in processing data used. This study used user-item interaction data and product reviews from Bukalapak. The experimental results showed that the proposed CF recommendation system had good accuracy with f-measure about 0.45 and quality improvement about 28%.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Clustering,Clustering methods,Collaborative filtering recommendations,K-means,Quality clustering,Quality improvement,Recommendation methods,Sparsity problems[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Clustering,Collaborative filtering,K-means++,KNN,Machine learning,Naïve Bayes,Recommendation system,Sentiment analysis[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISEMANTIC.2019.8884223[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]