• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.37-45
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• 2022

As of 2020, about 500 hours of videos are uploaded to YouTube, a representative online video platform, per minute. As the number of users acquiring information through various uploaded videos is increasing, online video platforms are making efforts to provide better recommendation services. The currently used recommendation service recommends videos to users based on the user's viewing history, which is not a good way to recommend videos that deal with specific purposes and interests, such as educational videos. The recent recommendation system utilizes not only the user's viewing history but also the content features of the item. In this paper, we extract the content features of educational video for educational video recommendation based on YouTube, design a recommendation system using it, and implement it as a web application. By examining the satisfaction of users, recommendataion performance and convenience performance are shown as 85.36% and 87.80%.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.112-120
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• 2011

This research aims to show that the context adaptation system can be used to develop practical context-aware applications by developing a restaurant recommendation system based on location-awareness. In this research, we have designed and implemented a location-aware restaurant recommendation system which provides a customized restaurant recommendation service based on the user's current context. The context-adaptation engine adapts the application program according to the policy file as contexts are changed, and the application provides restaurant recommendation service based on the changed context like location.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.33-43
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• 2017

The content curation service through big data analysis is receiving great attention in various content fields, such as film, game, music, and book. This service recommends personalized contents to the corresponding user based on user's preferences. The existing book curation systems recommended books to users by using bibliographic citation, user profile or user log data. However, these systems are difficult to recommend books related to character names or spatio-temporal information in text contents. Therefore, in this paper, we suggest a personalized book curation system based on integrated mining of a book. The proposed system consists of mining system, recommendation system, and visualization system. The mining system analyzes book text, user information or profile, and SNS data. The recommendation system recommends personalized books for users based on the analysed data in the mining system. This system can recommend related books using based on book keywords even if there is no user information like new customer. The visualization system visualizes book bibliographic information, mining data such as keyword, characters, character relations, and book recommendation results. In addition, this paper also includes the design and implementation of the proposed mining and recommendation module in the system. The proposed system is expected to broaden users' selection of books and encourage balanced consumption of book contents.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.40-51
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• 2011

The key issues of recommendation systems provide the contents satisfying the interests of users for the huge amounts of contents over internet. The existing recommendation system use the algorithms considering the users' profiles and context information to enhance the exactness of a recommendation. However, the existing recommendation system can't satisfy the requirements of service providers because the business models of service providers is not considered. In this paper, we propose the mobile recommendation system using the composite contexts and the recommendation weights applying the business model of service providers. The proposed system retrieves the contents of the contents providers using composite context information and apply the recommendation weights to recommend the suitable contents for the business models of service providers. Therefore, we provide the contents satisfying the consumption value of users and the business models of service providers to mobile users.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.49-56
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• 2024

There have been emerging many use-cases applying recommendation systems especially in online platform. Although the performance of recommendation systems is affected by a variety of factors, selecting appropriate features is difficult since most of recommendation systems have sparse data. Conventional matrix factorization (MF) method is a basic way to handle with problems in the recommendation systems. However, the MF based scheme cannot reflect non-linearity characteristics well. As deep learning technology has been attracted widely, a deep neural network (DNN) framework based collaborative filtering (CF) was introduced to complement the non-linearity issue. However, there is still a problem related to feature embedding for use as input to the DNN. In this paper, we propose an effective method using singular value decomposition (SVD) based feature embedding for improving the DNN performance of recommendation algorithms. We evaluate the performance of recommendation systems using MovieLens dataset and show the proposed scheme outperforms the existing methods. Moreover, we analyze the performance according to the number of latent features in the proposed algorithm. We expect that the proposed scheme can be applied to the generalized recommendation systems.

• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.112-123
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• 2005

The latest E-commerce sites provide personalized services to maximize user satisfaction for Internet user The collaborative filtering is an algorithm for personalized item real-time recommendation. Various supplementary methods are provided for improving the accuracy of prediction and performance. It is important to consider these two things simultaneously to implement a useful recommendation system. However, established studies on collaborative filtering technique deal only with the matter of accuracy improvement and overlook the matter of performance. This study considers representative attribute-neighborhood, recommendation textile set, and similarity grouping that are expected to improve performance to the recommendation agent system. Ultimately, this paper suggests empirical applications to verify the adequacy and the validity on this system with the development of Fashion Design Recommendation Agent System (FDRAS ).

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.35-48
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• 2017

There are many and various devices like sensors, displays, smart phone, etc. in smart environment. And contents can be provided by using these devices. Vast amounts of contents are provided to users, but in most environments, there are no regard for user or some simple elements like location and time are regarded. So there's a limit to provide meaningful contents to users. In this paper, I suggest the contents recommendation system that can recommend contents to users by reasoning context of users, devices and contents. The contents recommendation system suggested in this paper recommend the contents by calculating the user preferences using the situation reasoned with the contextual data acquired from various devices and the user profile received from the user directly. To organize this process, the method on how to model ontology with domain knowledge and how to design and develop the contents recommendation system are discussed in this paper. And an application of the contents recommendation system in Centum City, Busan is introduced. Then, the evaluation methods how the contents recommendation system is evaluated are explained. The evaluation result shows that the mean absolute error is 0.8730, which shows the excellent performance of the proposed contents recommendation system.