Dynamic convolutional neural network for eliminating item sparse data on recommender system
Several efforts have been conducted to handle sparse product rating in e-commerce recommender system. One of them is the inclusion of texts such as product review, abstract, product description, and synopsis. Later, it converted to become rating value. Previous researches have tried to extract these...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Universitas Ahmad Dahlan,
2018-11-28.
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| LEADER | 02073 am a22002893u 4500 | ||
|---|---|---|---|
| 001 | IJAIN_291_ijain_v4i3_p226-237 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Hanafi, Hanafi |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Suryana, Nanna |e author |
| 700 | 1 | 0 | |a Basari, Abdul Samad Hasan |e author |
| 245 | 0 | 0 | |a Dynamic convolutional neural network for eliminating item sparse data on recommender system |
| 260 | |b Universitas Ahmad Dahlan, |c 2018-11-28. | ||
| 500 | |a https://ijain.org/index.php/IJAIN/article/view/291 | ||
| 520 | |a Several efforts have been conducted to handle sparse product rating in e-commerce recommender system. One of them is the inclusion of texts such as product review, abstract, product description, and synopsis. Later, it converted to become rating value. Previous researches have tried to extract these texts based on bag of word and word order. However, this approach was given misunderstanding of text description of products. This research proposes a novel Dynamic Convolutional Neural Network (DCNN) to improve meaning accuracy of product review on a collaborative filtering recommender system. DCNN was used to eliminate item sparse data on text product review while the accuracy level was measured by Root Mean Squared Error (RMSE). The result shows that DCNN has outperformed the other previous methods. | ||
| 540 | |a Copyright (c) 2018 Hanafi Hanafi, Nanna Suryana, Abdul Samad Hasan Basari | ||
| 540 | |a https://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Recommender system; E-commerce; Convolutional Deep learning; Collaborative filtering | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n International Journal of Advances in Intelligent Informatics; Vol 4, No 3 (2018): November 2018; 226-237 | |
| 786 | 0 | |n 2548-3161 | |
| 786 | 0 | |n 2442-6571 | |
| 787 | 0 | |n https://ijain.org/index.php/IJAIN/article/view/291/ijain_v4i3_p226-237 | |
| 856 | 4 | 1 | |u https://ijain.org/index.php/IJAIN/article/view/291/ijain_v4i3_p226-237 |z Get Fulltext |