Feature Extracted Deep Neural Collaborative Filtering: Comparison
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Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by JiYoon Kim.

The electronic publication market is growing along with the electronic commerce market. Electronic publishing companies use recommendation systems to increase sales to recommend various services to consumers. However, due to data sparsity, the recommendation systems have low accuracy. Also, previous deep neural collaborative filtering models utilize various variables of datasets such as user information, author information, and book information, and these models have the disadvantage of requiring significant computing resources and training time for their training.

  • collaborative filtering
  • electronic publishing
  • feature extraction

1. Introduction

The number of Internet users constantly increases, and the use of the electronic commerce market is growing accordingly [1]. As a result, the electronic publication market, based on Internet technologies, is growing. General consumers use various platforms to purchase electronic books (e-books), and students use e-books for learning. Moreover, as recent studies have found that e-books can help students learn, student e-book use is expected to increase [2]. Therefore, a customized recommendation system that can help increase e-book sales is essential for the survival of companies.
Due to technological developments, online sales markets have grown, and corporations and researchers have studied various recommendation algorithms [3,4,5,6,7][3][4][5][6][7]. The collaborative filtering (CF) system, commonly used in recommendation systems, analyzes the correlation between the user and the product and recommends new products based on past experiences and behavior. Based on the rating of a certain product, CF uses the item-based CF to search for products similar to the corresponding product; the information of the user and that of users that purchased and assessed similar products constitutes user-based CF. The user-based CF predicts the user rating of a product that the user has never purchased [8,9][8][9]. However, a limitation of CF is that it consumes considerable hardware resources to execute the recommendation system. Therefore, studies on recommendation systems based on matrix decomposition have been conducted to reduce resource use. These propose efficient resource models by defining latent vector features between the user and the product. However, the matrix decomposition-based recommendation system has a data sparsity problem [10].
Therefore, to enhance the existing recommendation systems, recent studies have investigated deep-learning-based recommendation systems. The user–product data in the universal recommendation system are multidimensional data composed of numbers or texts [11]. Previous studies have used deep learning with multidimensional data to study regression, image and voice classification, and natural language processing and have demonstrated that deep learning outperforms previous algorithms [12]. In particular, the recommendation systems are characterized by deep-learning embedding [13] and multilayer perceptron [14] for processing the user and product data. The embedding transforms the input data into vectors to compute the similarity between the data. In addition, multilayer perceptron can analyze nonlinear data by using a hidden layer. Various deep-learning-based recommendation systems have been proposed based on these models. However, models that apply efficient product–user data feature extraction based on the nature of the deep-learning model are still lacking. Furthermore, presented deep learning-based recommendation systems have exploited diverse types of data such as user, product, and creator information to improve the accuracy of the models. However, various data require more computing resources, training times to train the model, and considerable time to optimize the parameters.

2. Recommendation Systems

Deep learning is continuously researched and thus is used in various sectors. For example, neural CF, a deep-learning-based recommendation model, was proposed in [15]. The model was used to interpret the complex relationship between the user and item data; the complex relationship is a disadvantage of the matrix factorization model [15]. In addition, the CF method (ConvNCF) uses an interactive map with various 2-dimensional convolution neural networks (CNNs) and an embedding layer to demonstrate that the deep-learning-based recommendation models outperformed the conventional models at the time [16]. ConvMF embedded CNNs to generate recommendations with user review data [17]. It was also used with continuous features, such as the user’s age, app installation, and session, and categorical features, such as the mobile device and demographics of the user. The corresponding model is accurate and expandable, and the input data features were improved [18]. In a novel personalized long short-term memory-based matrix factorization approach for online quality-of-service prediction, the latent features were extracted to represent many users and services. In addition, the newly input data were used to update the model [19] progressively. The deep hybrid CF approach for the service recommendation model used the similarity of the text information in the multilayer perceptron [20] and learned the nonlinear relationships of the services and mashups of the web service that retain complex call relationships [21]. In [22], an image-based service recommendation system was used to extract the features from JPEG-type image data, and a model that used a CNN and random forest ensemble algorithm was proposed. The dual-embedding-based deep latent factor model was proposed to obtain implicit feedback. The existing embedding of the recommendation system used the user- and item-embedding layers, and models that apply the user–item interaction into four types of embedding have also been proposed [23].

3. Feature Extraction Systems

In [24], a convolutional graph network was used to learn features from knowledge graphs instead of embeddings to improve the performance of the recommendation system. In addition, to enhance the convolutional neural networks gated recurrent units were used. In [25], the feature of personalized preference information was extracted, and the structure of the deep-learning model that could explicate the user’s decision-making was proposed. A feature learning process that overlooks the past interaction information and a model that applies attention was also presented. Chen et al. [26] proposed a heterogeneous information network model that uses deep learning. As a feature, rich secondary data containing the user review data and user rating were used; the secondary data were presented to the models that train with the features of entities through the network embedding process. Zeng et al. [27] proposed a deep-learning model for the recommendation system that uses the co-occurrence embedding structure with the rating, user, and item metrics to analyze the correlation between the user and item data [27]. Finally, in [28], the fusion recommendation model that utilizes the item rating and user review data was presented. One limitation of the traditional fusion recommendation model is its complexity; thus, a subnetwork that separated the user review data and rating data was used to reduce the complexity. Aside from the traditional deep-learning-based recommendation system structure that uses the multilayer perceptron, an immediate item was recommended using the fused features. Huang et al. [29] proposed a neural explicit factor model to increase the explainability of the recommendation systems of the existing CF models that use user and item data. The explainability of the item and user vectors was increased by applying the user-feature attention matrix and an n-item-feature quality matrix to the user–item rating matrix. Additionally, using a 1-dimensional CNN and feedforward neural network, the features from the item-feature vector, user, and item were extracted [29]. The proposed model is structurally closest to the deep neural CF model with feature extraction, but the deep neural CF model with feature extraction underwent the feature selection process of the user items, and the residual connection of the item data and the embedded users is performed for the feature map data that rearrange the selected features. Therefore, the role of the model is different, and the number of parameters used in the computation of deep learning shows a significant difference.

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