• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.113-118
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• 2015

Recommendation system is filtering for users require appropriate information from increasing information. Recommendation system is provides the information based on user information or content that information entered in the original through process of filtering through the algorithm. Recommend system is problems with Cold-start, and Cold-start is not enough information in the occurrences for new users of recommend system in the new information to the user when recommend. Cold-start is should meet to resolve the user of information and item information. In this paper, Suggest for movie recommendation system on collaborative filtering techniques and content-based filtering techniques based to a hybrid of a hybrid filtering techniques to solve problems in cold-start.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.125-134
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• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Information Systems Review
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• v.19 no.2
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• pp.71-94
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• 2017

Recommender systems reduce information overload and enhance choice quality. This technology is used in many services and industry. Previous studies did not consider recommendation quantity and the repetitive recommendations of an item. This study is the first to examine recommender systems by considering recommendation quantity and repetitive recommendations. Only a limited number of items are displayed in offline stores because of their physical limitations. Determining the type and number of items that will be displayed is an important consideration. In this study, I suggest the use of a user-based recommender system that can recommend the most appropriate items for each store. This model is evaluated by MAE, Precision, Recall, and F1 measure, and shows higher performance than the baseline model. I also suggest a new performance evaluation measure that includes Quantity Precision, Quantity Recall, and Quantity F1 measure. This measure considers the penalty for short or excess recommendation quantity. Novelty is defined as the proportion of items in a recommendation list that consumers may not experience. I evaluate the new revenue creation effect of the suggested model using this novelty measure. Previous research focused on recommendations for customer online, but I expand the recommender system to cover stores offline.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.9-15
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• 2017

According to a recent survey, most elementary and secondary school students nationwide are stressed out by their academic records. Furthermore most of high school students in Korea have to study under the great duress. Some of them who can't overcome the academic stress finalize their life by suiciding. A study has found that it is one of the leading causes of stimulating the thought of committing suicide in Korean high school students. So it is necessary to reduce the high school student's suicide rate. Main content of this research is to implement a personalized music recommendation system. Music therapy can help the student deal with the stress, anxiety and depression problems. Proposed system works as a therapist. The music choice and duration of the music is adjusted based on the student's current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

• Journal of Korean Library and Information Science Society
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• v.47 no.1
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• pp.279-303
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• 2016

This study proposes the Book-Curation service as part of the information service offered through school library websites. Also, this study aims to establish recommendation standards for curation prior to detailed system planning. For such service, the following tasks were carried out. First, the list of recommended books of existing systems were analyzed to identify the attributes that can be used for recommendation in the user and book information. Second, the analyzed attributes were utilized to establish 12 recommendation standards. Finally, a survey was carried out to identify the user preferences as to each standards. The results are as follows. First, the majority of students responded that curation service is necessary for using a library. Second, the top three standards are as follows: "best lending books based on the keywords of individual users"; "best lending books of the same year students"; "best lending books on the textbook-related reference booklist".

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.123-131
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• 2020

After observing that most tourists plan to complete their visits to multiple cultural heritage sites within one day, we surmised that for many museum visitors, the foremost thought is with regard to the amount of time is to be spent at each location and how they can maximize their enjoyment at a site while still balancing their travel itinerary? Recommendation systems in e-commerce are built on knowledge about the users' previous purchasing history; recommendation systems for museums, on the other hand, do not have an equivalent data source available. Recent solutions have incorporated advanced technologies such as algorithms that rely on social filtering, which builds recommendations from the nearest identified similar user. Our paper proposes a different approach, and involves providing dynamic recommendations that deploy social filtering as well as content-based filtering using term frequency-inverse document frequency. The main challenge is to overcome a cold start, whereby no information is available on new users entering the system, and thus there is no strong background information for generating the recommendation. In these cases, our solution deploys statistical methods to create a recommendation, which can then be used to gather data for future iterations. We are currently running a pilot test at Chao Samphraya national museum and have received positive feedback to date on the implementation.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.283-308
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• 2012

With explosive growth of information by recent advancements in information technology and the Internet, users need a method to acquire appropriate information. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Also, users and service providers are growing more and more interested in personalized information recommendation. This study designed and implemented personalized information recommendation system based on AR as a method to provide positive information service for information users as a method to provide positive information service. To achieve the goal, the proposed method overcomes the weaknesses of existing systems, by providing a personalized recommendation method for contents that works in a large-scaled data and user environment. This study based on the proposed method to extract rules from log files showing users' behavior provides an effective framework to extract Association Rule.

• The Korean Journal of Franchise Management
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• v.10 no.1
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• pp.19-32
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• 2019

Purpose - Since the opening of Korea's distribution market, the domestic network marketing market has been continuing to grow. In this context, research on network marketing independent operators, which plays the most important role in the network marketing industry, is insufficient. This study was to identify the effects of Independent Operator's Company Selection Attributions on the Economic and Non-Economic Satisfaction, Trust, and Recommendation. The results will provide strategic direction, theoretical and practical implications for companies and operators in the network marketing industry. Research design, data, and methodology - In order to verify the research hypotheses, the data were collected from Independent Operators of Network marketing industry using questionnaires. The pretest was conducted from January 8 to 19, 2018, and the main survey was conducted from February 1 to 28. A total of 210 questionnaires, of which 193 copies were collected. The data were analyzed with SPSS 21.0. and AMOS 21.0. Results - The results are as follows; product competitiveness and system competitiveness have significant effects on economic satisfaction and non-economic satisfaction. Economic and non-economic satisfaction have significant effects on business trust. Economic and non-economic satisfaction did not influence recommendation intention directly, but influence it indirectly. Business trust has a significant effect on business recommendation intention. Conclusions - After starting network marketing business as an independent operator, the competitiveness of the company is meaningless, and product competitiveness and system competitiveness are important factors for economic and non-economic satisfaction. Therefore, network marketing companies and independent operators should prioritize product competitiveness and system competitiveness between business development. The findings show that trust in the business is very important for active business Recommendation to others. Therefore, network marketing firms and independent operators need to make efforts to meet economic and non-economic satisfaction, which have a significant impact on business trust.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.711-728
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• 2023

This study aims to validate the user experience associated with the personalized recommendation systems of fashion vertical platforms. The investigation focused on women aged 18 to 30 with prior experience using personalized fashion recommendation systems. The collected data were analyzed using SPSS 26.0 and AMOS 26.0, and the outcomes can be summarized as follows. Firstly, the diversity and usefulness of information quality exerted a positive effect on use satisfaction. Secondly, the affirmative impact of the reliability of system quality on user satisfaction was established, although stability was not confirmed. Thirdly, the study identified a favorable connection between ease-of-use of service quality and user satisfaction, while the influence of tangibles was unsubstantiated. Fourthly, the degree of self-reference was found to have a positive effect on user satisfaction. Fifthly, a constructive relationship emerged between user satisfaction and both continuous-use intention and recommendation intention. Lastly, there was a significant difference in the magnitude of the effect of ease-of-use on satisfaction according to self-regulatory focus. The findings of this study hold the potential to enhance the explanatory and predictive power of the field of consumer behavior within the novel shopping landscape of fashion vertical platforms.