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Performance Comparison and Analysis Between Neural and Non-neural Autoencoder-based Recommender Systems
http://doi.org/10.5626/JOK.2020.47.11.1078
While deep neural networks have been bringing advances in many domains, recent studies have shown that the performance gain from deep neural networks is not as extensive as reported, compared to the higher computational complexity they require. This phenomenon is caused by the lack of shared experimental settings and strict analysis of proposed methods. In this paper, 1) we build experimental settings for fair comparison between the different recommenders, 2) provide empirical studies on the performance of the autoencoder-based recommender, which is one of the main families in the literature, and 3) analyze the performance of a model according to user and item popularity. With extensive experiments, we found that there was no consistent improvement between the neural and the non-neural models in every dataset and there is no evidence that the non-neural models have been improving over time. Also, the non-neural models achieved better performance on popular item accuracy, while the neural models relatively perform better on less popular items.
A CNN-based Column Prediction Model for Generating SQL Queries using Natural Language
Yoonki Jeong, Dongmin Kim, Jongwuk Lee
http://doi.org/10.5626/JOK.2019.46.2.202
To retrieve massive data using relational database management system (RDBMS), it is important to understanding of table schemas and SQL grammar. To address this issue, many studies have recently been carried out to generate an SQL query from a natural language question. However, the existing works suffer mostly from predicting columns at where clause and the accuracy is greatly reduced when there are multiple columns to be predicted. In this paper, we propose a convolutional neural network model with column attention mechanism that effectively extracts the latent representation of input question which helps column prediction of the model. The experiment shows that our model outperforms the accuracy of the existing model (SQLNet) by 6%.
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