• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Information Systems Review
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• v.24 no.3
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• pp.69-91
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• 2022

The video recommendation services help to save the user's information search time in the overflowing online information, and algorithms for more efficient and accurate recommendation are continuously developed. In particular, TikTok has the largest number of users in the short video industry due to its unique recommendation algorithms. In this study, by applying a privacy calculation model, the research tried to compare users' responses to each type of TikTok's recommendation service. Users are well aware of the privacy concerns and benefits of TikTok's recommendation service. Although there is a risk, it was found that users continue to use TikTok's recommendation service because the benefits are greater.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.183-191
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• 2017

In order to secure the convenience of information retrieval by users of scientific information service platforms and to reduce the time required to acquire the proper information, this study proposes an optimized content recommendation algorithm among the algorithms that currently provide service menus and content information for each service, and conducts comparative evaluation on the results. To enhance the recommendation accuracy, users' major items were added to the original algorithm, and performance evaluations on the recommendation results from the original and optimized algorithms were performed. As a result of this evaluation, we found that the relevance of the content provided to the users through the optimized algorithm was increased by 21.2%. This study proposes a method to shorten the information acquisition time and extend the life cycle of the results as valuable information by automatically computing and providing content suitable for users in the system for each service menu.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.185-193
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• 2010

With the emergence of ubiquitous computing era, various models for providing personalized service have been proposed, and, especially, several recommendation service schemes have been proposed to give tailored services to users proactively. However, the previous recommendation service schemes utilize a wide range of data without and filtering and consider the limited context-aware information to predict user preferences so that they are not adequate to provide personalized service to users. In this paper, we propose an adaptive recommendation service scheme which proactively provides suitable services based on the current context. We use accumulated interaction contexts (IC) between users and devices for predicting the user's preferences and recommend adaptive service based on the current context by utilizing clustering and collaborative filtering. The clustering algorithm improves efficiency of the recommendation service by focusing and analyzing the data that is collected from the locations nearby the users. Collaborative filtering guarantees an accurate recommendation, even when the data is insufficient. Finally, we evaluate the performance and the reliability of the proposed scheme by simulations.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.35-43
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• 2017

In IoT(Internet of Things) environment, users want to receive customized service by users' personal device such as smart watch and pendant. To fulfill this requirement, the mobile device should support a lot of functions. However, the miniaturization of mobile devices is another requirement and has limitation such as tiny display. limited I/O, and less powerful processors. To solve this limitation problem and provide customized service to users, this paper proposes a collaboration system for sharing various computing resources. The paper also proposes the method for reasoning and recommending suitable resources to compose the user-requested service in small device with limited power on expected time. For this goal, our system adopts MBTI(Myers-Briggs Type Indicator) to analyzes user's behavior pattern and recommends personalized resources based on the result of the analyzation. The evaluation in this paper shows that our approach not only reduces recommendation time but also increases user satisfaction with the result of recommendation.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.202-207
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• 2023

In this paper, we discuss the design and implementation of a recommendation platform actually built in the field. We survey deep learning-based recommendation models that are effective in reflecting individual user characteristics. The recently proposed RNN-based sequential recommendation models reflect individual user characteristics well. The recommendation platform we proposed has an architecture that can collect, store, and process big data from a company's commercial services. Our recommendation platform provides service providers with intuitive tools to evaluate and apply timely optimized recommendation models. In the model evaluation we performed, RNN-based sequential recommendation models showed high scores.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5344-5356
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• 2018

Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations in the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• Journal of the Ergonomics Society of Korea
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• v.27 no.4
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• pp.85-94
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• 2008

Internet shopping has been getting more rousing due to extension of supply with PC(personal computer) and a rapid rise of use of internet. Some companies have been continually researching in how to serve individuals with each ordered information, which aimed at getting ordinary customers to induce to be loyal customers. For that, there is progress of a service of a web-recommendation which considers individual attribution. This study is suggested a method which is a service of the web-recommendation by access to sensibility ergonomics approach. Previous studies established that service had a weak point. It did not manage to realize new needs of customers. Proposed service of the web-recommendation has been designed, which preferentially propose goods included customer's sensibility to the customer who wants it. This study is expected that it will encourage a rise of products' purchasing power of customers, make an increase in a profit of both sellers and people who operate electric commercial and satisfaction of customers will go up in the same. Also, products accord with sensibility of customers will be recommended customers by the suggested service of the web-recommendation. In addition, there will be a decline of time-consuming about making a choice among some products.

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.263-270
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• 2020

The growth of the mobile app markets has made it popular among people who recommend relevant information about restaurants. The recommendation service app based on AI Chatbot is that it can efficiently manage time and finances by making it easy for restaurant consumers to easily access the information they want anytime, anywhere. Eating out consumers use smartphone applications for finding restaurants, making reservations, and getting reviews and how to use them. In addition, social attention has recently been focused on the research of AI chatbot. The Chatbot is combined with the mobile messenger platform and enabling various services due to the text-type interactive service. It also helps users to find the services and data that they need information tersely. Applying this to restaurant recommendation services will increase the reliability of the information in providing personal information. In this paper, an artificial intelligence chatbot-based smartphone restaurant recommendation app using personalization information is proposed. The recommendation service app utilizes personalization information such as gender, age, interests, occupation, search records, visit records, wish lists, reviews, and real-time location information. Users can get recommendations for restaurants that fir their purpose through chatting using AI chatbot. Furthermore, it is possible to check real-time information about restaurants, make reservations, and write reviews. The proposed app uses a collaborative filtering recommendation system, and users receive information on dining out using artificial intelligence chatbots. Through chatbots, users can receive customized services using personal information while minimizing time and space limitations.