Dynamic convolutional neural network for eliminating item sparse data on recommender system

(1) * Hanafi Hanafi Mail (Amikom University, Indonesia)
(2) Nanna Suryana Mail (Univeristi Teknikal Malaysia Melaka, Malaysia)
(3) Abdul Samad Hasan Basari Mail (Univeristi Teknikal Malaysia Melaka, Malaysia)
*corresponding author


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.


Recommender system; E-commerce; Convolutional Deep learning; Collaborative filtering




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