• Journal of Digital Convergence
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• v.14 no.8
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• pp.313-318
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• 2016

The recommendation service is changing from client-server based internet service to social networking. Especially in recent years, it is serving recommendations with personalization to users through crowdsourcing and social networking. The social networking based systems can be classified depending on methods of providing recommendation services and purposes by using memory and model based collaborative filtering. In this study, we proposed the social network based sensibility design recommendation using associative user. The proposed method makes {user - associative design} matrix through the social network and recommends sensibility design using the memory based collaborative filtering. For the performance evaluation of the proposed method, recall and precision verification are conducted. F-measure based on recommendation of social networking is used for the verification of accuracy.

• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.61-69
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• 2014

Collaborative filtering recommendation creates similar item subset or similar user subset based on user preference about items and predict user preference to particular item by using them. Thus, if preference matrix has low density, reliability of recommendation will be sharply decreased. To solve these problems we suggest Hybrid Preference Prediction Technique Using Weighting based Data Reliability. Preference prediction is carried out by creating similar item subset and similar user subset and predicting user preference by each subset and merging each predictive value by weighting point applying model condition. According to this technique, we can increase accuracy of user preference prediction and implement recommendation system which can provide highly reliable recommendation when density of preference matrix is low. Efficiency of this system is verified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than Hao Ji's technique when preference matrix sparsity is more than 84% through experiment.

• 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 the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.99-105
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• 2021

In addition to the OTT video production service represented by Nexflix and YouTube, a personalized recommendation system for content with artificial intelligence has become common. YouTube's personalized recommendation service system consists of two neural networks, one neural network consisting of a recommendation candidate generation model and the other consisting of a ranking network. Netflix's video recommendation system consists of two data classification systems, divided into content-based filtering and collaborative filtering. As the online platform-led content production is activated by the Corona Pandemic, the field of virtual influencers using artificial intelligence is emerging. Virtual influencers are produced with GAN (Generative Adversarial Networks) artificial intelligence, and are unsupervised learning algorithms in which two opposing systems compete with each other. This study also researched the possibility of developing AI platform based on individual recommendation and virtual influencer (metabus) as a core content of OTT in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1435-1442
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• 2009

This paper proposes a music recommendation system for a driver in vehicle. The proposed system provides (selects and plays) a music to a driver in vehicle in real-time manner by inferring his preference based on physical, environmental, and personal information. Pulse data as physical information, age and biorhythm as personal information, and time as environmental information are used to infer a driver's and thus recommend a music. Experimental results showed that the proposed system could provide better satisfaction to a driver on the recommended music compared to the conventional approach.

• Korean Management Science Review
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• v.23 no.3
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• pp.183-194
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• 2006

In this paper, we propose a customer profiling technique based on customer behavior for personalized products recommendation in Internet shopping mall. The proposed technique defines customer profile model based on customer behavior Information such as click data, buying data, market basket data, and interest categories. We also implement CBCPT(customer behavior based customer profiling technique) and perform extensive experiments. The experimental results show that CBCPT has higher MAE, precision, recall, and F1 than the existing other customer profiling technique.

• Asia pacific journal of information systems
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• v.12 no.4
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• pp.121-137
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• 2002

Researches on P2P, the information sharing model from person to person, up to date have focused on the technical side and there have been lacking of the business side researches such as customer loyalty. Considering the problem, this study tries to examine empirically in what way the factors of service, market, and customer affect the service loyalty of P2P sites. Results of the study show that the three factors have statistically significant effects on the service loyalty in general. In particular, the results uncover that those factors have different impacts on the reuse intention and the recommendation intention, which are the two measures of service loyalty. For example, the cost of service use affects the reuse intention significantly whereas the same element does not affect the recommendation intention. Interestingly, some of the results are not coincide with the results of previous studies and do not meet general expectation. For example, users' tendency of pursuing variety has positive impacts on the service loyalty, especially, the intention of recommendation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.19-27
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• 2011

Recent development of information technologies produces a lot of community services. Social Network Service is one of the community services on the world wide webs. In the Social Network Service, a user can register other users as friends and enjoy communication through a virtual message. Previous researches show a few social service methods using manually generated tagging. However, the manual social tagging is not widely used in many social network services. Moreover, they do not consider ubiquitous computing environment. We propose a recommendation method for social service using contexts in ubiquitous environment. Our method scores documents based on context tags and social network services. Our social scoring model is computed by both a tagging score of a document and a tagging score of a document that was tagged by a user's friends.

• Korea Journal of Hospital Management
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• v.22 no.3
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• pp.31-45
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• 2017

The research is to examine medical service quality factors affecting choice of hospital(revisiting intention, and recommendation) in large general hospitals based on the SERVQUAL model. The study have surveyed 400 respondents in Gangbuk-gu not having any tertiary hospital. The main results of the analyses indicate: 1) 'assurance' and 'empathy' of medical service are basically, positively affect revisiting intention and recommendation; 2) 'empathy' is the most important factor affecting revisiting intention; and 3) 'tangibility' significantly affects recommendation of general hospitals to other people. The study suggests that it is necessary to pay more attention on 'empathy' among SERVQUAL factors to increase satisfaction of patients and to find better ways of improving medical service quality.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.