• Korean Management Science Review
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• v.17 no.3
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• pp.19-30
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• 2000

Customization and personalization services are considered as a critical success factor to be a successful Internet store or web service provider. As a representative personalization technique, personalized recommendation techniques are studied and commercialized to suggest products or services to a customer of Internet storefronts based on demographics of the customer or based on an analysis of the past purchasing behavior of the customer. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customers data. however, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed knowledge base. In this paper, we proposed a marketing rule extraction technique for personalized recommendation on Internet storefronts using market basket analysis technique, a well-known data mining technique. Using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store. An experiment has been performed to evaluate the effectiveness of proposed approach comparing with preference scoring approach and random selection.

• Journal of KIISE
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• v.44 no.6
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• pp.621-627
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• 2017

Collaborative filtering is a technique that effectively recommends unrated items for users. Collaborative filtering is based on the similarity of the items evaluated by users. The existing top-N recommendation methods are based on pair-wise and list-wise preference models. However, these methods do not effectively represent the relative preference of items that are evaluated by users, and can not reflect the importance of each item. In this paper, we propose a new method to represent user's latent preference by combining an existing preference model and the notion of inverse user frequency. The proposed method improves the accuracy of existing methods by up to two times.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.51-63
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• 2003

Collaborative filtering is one of the methodologies that are most widely used for recommendation system. It is based on a data matrix of each customer's preferences of products. There could be a lot of missing values in such preference data matrix. This incomplete data is one of the reasons to deteriorate the accuracy of recommendation system. There are several treatments to deal with the incomplete data problem such as case deletion and single imputation. Those approaches are simple and easy to implement but they may provide biased results. Multiple imputation method imputes m values for each missing value. It overcomes flaws of single imputation approaches through considering the uncertainty of missing values. The objective of this paper is to suggest multiple imputation-based collaborative filtering approach for recommendation system to improve the accuracy in prediction performance. The experimental works show that the proposed approach provides better performance than the traditional Collaborative filtering approach, especially in case that there are a lot of missing values in dataset used for recommendation system.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.113-119
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• 2020

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

• Asia Pacific Journal of Business Review
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• v.5 no.1
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• pp.21-36
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• 2020

This paper examines the effect of the choice properties of food carving decoration lectures on the recommendation and revisiting intention. As the culinary industry grows, consumers seek not only the value of satiety through food but also the value of aesthetics. They perceived satisfaction from the taste and appearance of the food as well as the interior of the restaurant and the service. Food carving is an important means of fulfilling consumer satisfaction value which is ever-changing. This study attempts to analyze the relationship between the choice properties of food carving lectures and the course recommendation and revisiting intention. This study hypothesis was formulated and the survey was conducted on 125 respondents who had experienced food carving lectures. The reliability and validity of measurement items were verified through Cronbach's Alpha and factor analysis. As a result, all measurement items showed no abnormality. The results of the analyses are as follows. The education satisfaction, education commitment, and brand image, the choice properties of food carving decoration lecture, were found to have a positive effect on the recommendation intention. The results also showed that the education commitment and brand image of food carving decoration lectures were positively associated with revisiting intention. Lastly, the implications of these findings were suggested and for future research were discussed.

• Journal of Integrative Natural Science
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• v.11 no.3
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• pp.131-138
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• 2018

In this paper, we have implemented speeches that utilized the emotion information of the user's speech and image matching and recommendation system. To classify the user's emotional information of speech, the emotional information of speech about the user's speech is extracted and classified using the PLP algorithm. After classification, an emotional DB of speech is constructed. Moreover, emotional color and emotional vocabulary through factor analysis are matched to one space in order to classify emotional information of image. And a standardized image recommendation system based on the matching of each keyword with the BM-GA algorithm for the data of the emotional information of speech and emotional information of image according to the more appropriate emotional information of speech of the user. As a result of the performance evaluation, recognition rate of standardized vocabulary in four stages according to speech was 80.48% on average and system user satisfaction was 82.4%. Therefore, it is expected that the classification of images according to the user's speech information will be helpful for the study of emotional exchange between the user and the computer.

• Journal of Korea Multimedia Society
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• v.20 no.4
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• pp.704-712
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• 2017

Various researches on content sensibility with the development of digital contents are under way. Emotional research on fonts is also underway in various fields. There is a requirement to use the content expressions in the same way as the content, and to use the font emotion and the textual sensibility of the text in harmony. But it is impossible to select a proper font emotion in Korea because each of more than 6,000 fonts has a certain emotion. In this paper, we analysed emotional classification attributes and constructed the Hangul font recommendation system. Also we verified the credibility and validity of the attributes themselves in order to apply to Korea Hangul fonts. After then, we tested whether general users can find a proper font in a commercial font set through this emotional recommendation system. As a result, when users want to express their emotions in sentences more visually, they can get a recommendation of a Hangul font having a desired emotion by utilizing font-based emotion attribute values collected through the crowdsourced method.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.494-507
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• 2020

This study proposed the movie recommendation system based on the user's personal information and movies rated using the method of k-clique and normalized discounted cumulative gain. The main idea is to solve the problem of cold-start and to increase the accuracy in the recommendation system further instead of using the basic technique that is commonly based on the behavior information of the users or based on the best-selling product. The personal information of the users and their relationship in the social network will divide into the various community with the help of the k-clique method. Later, the ranking measure method that is widely used in the searching engine will be used to check the top ranking movie and then recommend it to the new users. We strongly believe that this idea will prove to be significant and meaningful in predicting demand for new users. Ultimately, the result of the experiment in this paper serves as a guarantee that the proposed method offers substantial finding in raw data sets by increasing accuracy to 87.28% compared to the three most successful methods used in this experiment, and that it can solve the problem of cold-start.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.265-273
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• 2012

It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also proposed the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.270-275
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• 2008

Under ubiquitous environment, recommendation system is using the collaborative filtering methods by quantifying context information, but insufficient context information can cause inaccurate recommendation result. In order to solve such problems, the researcher used context information and user's profile. But service history information in users' profiles can have the problems of being influenced by change of the user's taste or fashion as time passes by. In addition, context information and user's profile can't be properly inter-locked according to situation, which can cause inaccurate predictability. In this paper, in case a user's taste or fashion is changed as time passes by, the researcher didn't apply bundled-up value to the user's profile but applied different weight according to change of time. And the researcher could solve the problem that context information and a user's profile can't be properly inter-locked according to situation by applying different weight to the result gained by means of collaborative filtering and then by unifying it. In such ways, the researcher could improve predictability.