# Developing Data-Conscious Deep Learning Models for Product Classification
## 2 Related Work  
Product classification can be an application of text classification problem, if we consider only text data for product information. Early studies on automatic product classification exploited the traditional text classifiers such as Naive Bayes, SVM (support vector machine), and KNN (k-nearest neighbors) [13; 14; 15]. [PERSON], [PERSON] et al. [16] exploited a lexical similarity to improve the disambiguation of product taxonomy and suggested an ontology-based approach using WordNet. [PERSON], [PERSON], et al. [17] also suggested an ontological approach to classify the products, and they exploited a set of ontological data dictionaries for keyword-based product classification.  
Recently, various deep learning models have been applied in studies related to product classification. Table 1 summarizes representative related works and the deep learning models or classifiers used therein. It also summarizes whether the approach of each work was to develop a new model or to tune the hyperparameters of an existing model, as mentioned in the previous section.  
[PERSON], [PERSON] et al. [3] proposed an approach based on multi-CNN and multi-LSTM models (flat models). Hierarchical and flat classification schemes were compared. The results showed that the multi-CNN and multi-LSTM were more robust than the bag-of-word model (hierarchical model). The paper proposed an approach based on multi-CNN and multi-LSTM and a novel method that combines structured attributes with the deep learning models.  
[PERSON], [PERSON] et al. [4] classified product categories using product title data in Japanese, which is an agglutinative language. An attention conventional neural network (ACNN) was proposed using a conventional CNN model and gradient boosted tree (GBT) [18]. In the ACNN, a context encoder and an attention mechanism exist in the left and right modules, respectively. Moreover, higher weights are assigned to certain parts of the inputs through embedding. This plays the role of emphasizing tokens that have high correlations semantically.  
[PERSON], [PERSON] et al. [1] discussed data preprocessing to improve classification performance by increasing the quality of data. In that study, noise data were removed using various methods. The imbalance of data was quantified, and experiments were conducted using a basic algorithm such as Naive Bayes (NB) [13] and by additionally applying CNN and GBT. They indicated that performance could be improved further using a method that deletes noise data as stopwords. The related works mentioned above regard automatic product classification as a classification problem [19]. Furthermore, a commonality between these works is that they discussed the effect of various input data on classification models.  
As deep learning technology has been actively researched in image processing and recognition areas, some studies suggested to exploit the product image data for product classification [20; 21]. [PERSON], [PERSON] et al. [20] proposed a RNN-based DeepCNN model for product classification using real data from their own shopping mall. They used text data such as product names and brand names, and the use of image data was also suggested. Their data were imbalanced across the categories like ours. Note that data imbalance problems are frequently observed in other deep learning based studies as well [2; 8]. [PERSON], [PERSON] et al. [21] proposed a multi-modality framework using VGG, a kind of CNN model, for image data as well as text data of product information.