Involve Convolutional-NN to Generate Item Latent Factor Consider Product Genre to Increase Robustness in Product Sparse Data for E-commerce Recommendation. (May 2019) - Hanafi,; Suryana, N; Basari, A S H
Abstract: Online shopping also popular named e-commerce business need a computer machine to provide product information for customer or buyer candidate. Relevant information served by ecommerce system engine famous dubbed recommender system. It will impact seriously in increasing of marketing target achievement. The character of information in ecommerce have to be specific, personalized, relevant and fit according to customer profiling. There are four kind of recommender system to provide ecommerce recommender system, however only one model that most successful to applied in real ecommerce industry that namely collaborative filtering. These approaches rely on rating as basic calculation to generate product recommendation. However, just a little number of rating that given by customer reference to several convince datasets. The problem causes of sparse product rating, it will bring the impact to product recommendation accuracy. Sometime, in extreme condition impossible to generate product recommendation. Some work efforts have been developing to tackle lack of rating, one of them is considering to involving text sentences document such as text genre, product review, abstract, product description, synopsis, etc. All of material text sentences useful as raw component to predict the product rating. Reference previous work method aims extract text sentences document such as product review to become rating value based on bag of word and word order, sometimes they have got the mis in deeper understanding of text sentences description about product. Therefore, it influenced the result of predict the rating was inaccurate. In this research, author proposed novel method enhance variant of convolutional neural network dubbed dynamic convolutional neural network to improve scalability and increase deeper understanding to increase accuracy level. Based on our experiment, our model outperforms over existing state of the art in previous work in extracting text sentences product description based on evaluation approach uses RMSE.