Muhammad Nugraha Mahardhika, . (2023) SISTEM REKOMENDASI PRODUK MENGGUNAKAN IMPLICIT FEEDBACK BERBASIS COLLABORATIVE FILTERING PADA E-COMMERCE. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
Currently, the Indonesian people on buy and sell activities depend on e-commerce. The high growth of e-commerce produces transaction data on a massive scale can be used as a marketing strategy by companies, one of which is the Recommendation System. Recommendation System is a tool for estimate interested product based on matching the characteristics of each user with machine learning. Recommendation systems generally use collaborative filtering explicit feedback as a value of user interest on product. However, this causes data limitation problems (cold-start) because only based on transaction data that has been rated by the user. Instead of using explicit feedback, other solutions can use implicit feedback to avoid cold-start problems. By using implicit feedback, system can predict based on the number of user transactions for stores and product category. In this study, Singular Value Decomposition (SVD) is used as a matrix factorization model algorithm to find similarity between one and another user based on the feedback value. The results of the model show good performance with score RMSE ± 0,865 and MAE ± 0,508.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1910314030] [Pembimbing: Fajar Rahayu] [Penguji 1: Ferdyanto] [Penguji 2: Achmad Zuchriadi] |
| Uncontrolled Keywords: | Recommendation System, Cold-start, Matrix Factorization, Singular Value Decomposition (SVD), Machine Learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Teknik > Program Studi Teknik Elektro (S1) |
| Depositing User: | Muhammad Nugraha Mahardhika |
| Date Deposited: | 21 Jul 2023 04:36 |
| Last Modified: | 25 Jul 2023 06:12 |
| URI: | http://repository.upnvj.ac.id/id/eprint/25530 |
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