• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.29-39
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• 2020

Online communities, such as Internet portal sites and social media, have become popular since they allow users to share opinions and to obtain information anytime, anywhere. Accordingly, an increasing number of opinions are manipulated to the advantage of particular groups or individuals, and these opinions include falsified product reviews and political propaganda. Existing detection systems are built upon the characteristics of manipulated opinions for one particular time period. However, manipulation tactics change over time to evade detection systems and to more efficiently spread information, so detection systems should also evolve according to the changes. We therefore propose a system that helps observe and trace changes in manipulation tactics. This system classifies opinions into clusters that represent different tactics, and changes in these clusters reveal evolving tactics. We evaluated the system with over a million opinions collected during three election campaigns and found various changes in (i) the times when manipulations frequently occur, (ii) the methods to manipulate recommendation counts, and (iii) the use of multiple user IDs. We suggest that the operators of online communities perform regular audits with the proposed system to identify evolutions and to adjust detection systems.

• Korean Journal of Health Education and Promotion
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• v.17 no.1
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• pp.131-154
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• 2000

This study was conducted to provide basic data for development of public information program and public policy which could prevent the medicine abuse in Korea, examining the level of knowledge of medicine and its related factors. Data were collected from the 2,011 residents who live in mtropolitan cities, large-sized cities, small and medium cities, and small towns The results of this study are summarized as follows. 1) In case of purchasing of medicines in pharmacy, 67.3％ of the respondents chose the medicines through recommendations of the professionals such as pharmacists and doctors, while 32.7％ of the respondents chose the medicine through self-judgement, advertizing, or recommendation of relative. 2) 64.7％ of the respondents obtained the information on medicines through TV. It appeared to be higher in the groups of female of the twenties, the unmarred, a brother and sister threesome, highschool graduates, housewives, residents in small and medium cities, atheists, and the middle class, displaying the significant difference from the other groups. 3) 40.5％ of the respondents recognized the side effect of the medicine when they took the medicine, while 34.4％ did not recognize it. The rate of experience in the side effect was 39.7％. The informations on the medicine abuse and the risk of addiction were obtained through broadcast media (47.9％), publications (12.1％), and health professionals (11.6％). 4) 81.1％ of the respondents experienced taking of the fatigue relieving medicine. The experience in taking of the fatigue relieving medicine appeared to be higher in the groups of the forties. the married. a brother and sister threesome. highschool graduates. persons engaging in farming, livestock raising, and forestry, the residents in small towns, and Christians. Each group displayed the significant difference from the other groups. 5) According to the level of knowledge of medicines, the respondents marked average 11.7 ± 3.76 points on the base of 24 points. It appeared to be higher in the groups of female of the twenties, a brother and sister foursome, college graduates, teachers, Catholics, and the middle class, displays the significant difference from the other groups. 6) According to the experience in taking of health medicine and health food, 81.1％ of respondents had the experience in taking ‘the fatigue relieving medicine’; 72.4％ ‘carrot or vegetable juice’; 69.5％ ‘ginseng’; 63.0％ ‘mushroom’; 42.5％ ‘dog meat’; 38.0％ ‘aloe’; 36.4 ‘deer antlers’; 11.4％ ‘snake’; 2.0％ ‘the penis of a fur seal’. 7) The factors influencing the level of knowledge of medicine include experiences in taking of the tonic, the fatigue relieving medicine, and the nutritive medicine, economic status, the number of brothers and sisters, education level, marital status, father's education level, and age. The factors influencing the experience in side effect of medicine are experiences in taking of the fatigue relieving medicine, the nutritive medicine, and the tonic, sex, age, education level, father's education level, marital status, economic status, religion, and the number of brothers and sisters. In conclusion, it is estimated that the level of knowledge of medicines is significantly low in Korea. Especially, it is found out that workmen, students, the upper class, the class of low education level, and persons engaging in farming, livestock raising, and forestry neglect importance of knowledge of medicine. Therefore, it is necessary for public authority, associations related, and health professionals to develop programs for public information and education to help people obtain basic knowledge of medicine.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.141-148
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• 2014

As the quality of the medical treatment service provided by large hospitals grow, the number of patients utilizing the facilities is increasing dramatically. Various studies such as order communication system and treatment guidance system are under their process in order to shorten the waiting time for patients. However, the existing methods assign the treatments in successive order without recognizing the situation of each treatment, therefore increasing a patient's standby time at a hospital. This paper proposes a context-aware treatment guidance system, which recognizes the previously undermined estimated waiting time of each treatment for a patient and recommends a treatment with shorter estimated sojourn time first. This context-aware treatment guidance system provides detailed information of treatment services based on the recommended order of treatments to a patient's smartphone. By utilizing the context-aware treatment guidance system introduced in this paper, patients can reduce their standby time at hospitals to the minimum while hospitals can efficiently service more patients at the same amount of time. The proposed context-aware treatment guidance system proves to be outstanding in treatment order recommendation through comparisons to previously used methods.

• The Journal of Information Systems
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• v.27 no.2
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• pp.157-173
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• 2018

Purpose Product characteristics and price value in website have strongly effects on customer satisfaction. Especially, in the online shopping site, the scarcity limits the customer's opportunity to purchase the product. Thus scarcity has been proposed as a important factor that makes the customer highly aware of the merchantability of the product. The scarcity in the web store is used as an important variable to make purchasing decisions of users easier by psychological pressure. In the case of scarce products with price discounts in online commerce, advertising formats that highlight scarcity value in the web commerce market are very effective in enhancing purchase intentions of consumers. Unlike offline stores, the importance of scarcity becomes more important when reflecting the characteristics of online commerce. Therefore, this study intends to confirm the influence of the degree of price discounts and scarcity information presented by Web sites on consumer purchase behavior in Web purchase behavior. Design/methodology/approach This study conducted a web-based experimental study on price sensitivity and price discount. Therefore, we created experimental web-sites that offer two stimuli according to the discount rate. The 200 respondents were randomly assigned. The stimuli were fictitious based on tourism products. The first stimulus presented the price discount(15% discount) with basic explanation about the package of the tourist package. The stimuli assigned to the second group were used for groups with high price discount intensity(65% discount). In this way, the two stimuli clearly distinguished the level of price discount intensity. This paper conducted t-test analysis and structural equation to analyze the experiemental results after confirming the reliability and validity. Findings The results of this study are as follows. The difference in price discount intensity (15% vs 65%) with scarcity showed the mean difference among all the variables. Therefore, this study concluded that there is a significant difference between the price discount of 15% and 65% for the acquisition value and transaction value of users. In particular, consumers' purchase intention is greater and product recommendation intensity is stronger when the price discount is 65%. As a result, the high degree of the price discount intensity with scarcity exerts a greater influence on consumers' purchase intentions. Product scarcity also have a significant impact on perceived value of users. Therefore, purchase intention of customers increases when perceived value increases their profit and pleasure feeling.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.511-516
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• 2006

In this raper, we propose the Sequentiel Couleur Avec Memoire or Sequential Color with Memory (SECAM) video encoder system using modified anti-cloche filters and the SECAM video decoder system using a band pass filter (BPF) and an error-free square root. The SECAM encoder requires an anti-cloche filter recommended by International Telecommunication Union-Recommendation (ITU-R) Broadcasting service Television (BT) 470. However, the design of the anti-cloche filter is difficult because the frequency response of the anti-cloche filter is very sharp around rejection-frequency area. So, we convert the filter into a hish pass filter (HPF) by shifting the rejection frequency of 4.286MHz to 0Hz frequency. The design of HPF becomes very easy, compared to that of the anti-cloche filter. The proposed decoder also uses an error-free square root, two differentiators and trigonometric functions to extract color-component information of Db and Dr accurately from frequency modulation (FM) signals in SECAM systems. Also, the BPF in decoder it used for removing color noise in chrominance and dividing CVBS into chrominance and luminance. The proposed systems are experimentally demonstrated with Altera FPGA APEX20KE EP20K1000EBC652-3 device and TV sets.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.1
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• pp.5-20
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• 2020

The study aimed at identifying the causes of the high level of health application but the low level of use. In other words, the effects of user's innovation resistance (use barrier, value barrier, risk barrier, traditional barrier, image barrier, etc.) were examined. For this study, 378 valid responses were collected by conducting surveys with college students. Findings indicated the higher the level of image barrier of the user, the higher the degree of innovation resistance for the health application, and the higher the degree of innovation resistance, the lower intention of continuous use and recommendation. In addition, the level of use barriers, value barriers and traditional barriers did not have a significant effect on the degree of innovation resistance, suggesting that users familiar with smartphones have low resistance to health applications. The study deepens the theoretical discussion about the adoption and continuous use of new technologies by explaining the use of health applications in the theory of innovation resistance. The findings of the study provide the practical implications that lowering the image barriers rather than the usage barriers, value barriers and traditional barriers will be effective for the adoption and continuous use of health applications.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.451-462
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• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.1-14
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• 2007

Character images for the background screen of cell phones are one of the fast growing sectors of the mobile content market. However, character image buyers currently experience tremendous difficulties in searching for desired images due to the awkward image search process. Content-based image retrieval (CBIR) widely used for image retrieval could be a good candidate as a solution to this problem, but it needs to overcome the limitation of the mobile Internet environment where an initial query set (IQS) cannot be easily provided as in the PC-based environment. We propose a new approach, IQS-AutoGen, which automatically generates an initial query set for CBIR on the mobile Internet. The approach applies the collaborative filtering (CF), a well-known recommendation technique, to the CBIR process by using users' preference information collected during the relevance feedback process of CBIR. The results of the experiment using a PC-based prototype system show that the proposed approach successfully satisfies the initial query requirement of CBIR in the mobile Internet environment, thereby outperforming the current image search process on the mobile Internet.

• Journal of Korean Medicine Rehabilitation
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• v.31 no.4
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• pp.129-143
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• 2021

Objectives This study was aimed to evaluate clinical practice guidelines (CPGs) of traffic injuries, which has already been developed at domestic or outside of country, and to explore the Korean medical treatments included in the CPGs. Methods Twelve electronic databases (PubMed, Cochrane library, China National Knowledge Infrastructure [CNKI {Chinese Academic Journals, CAJ}], Research Information Sharing Service [RISS], Oriental Medicine Advanced Searching Integrated System [OASIS], KoreaMed, Korean Medical Guideline Information [KoMGI), National Guideline Clearinghouse [AHRQ], Core Outcome Measures in Effectiveness Trials Initiative Website [COMET], Turning Research into Practice [TRIP], The National Institute for Health and Care Excellence [NICE], and Medical Research Information Center [MedRIC]) up to July 2021 were searched. Only systematically developed CPGs for traffic injuries were selected and appraised. The appraisal was conducted by using Appraisal of Guidelines for Research & Evaluation (AGREE) II tool. Results Six CPGs were included and evaluated. All CPGs were appraised as highly recommended because they exceeded 60% in more than 4 among 6 domains of AGREE II, including domain of 'rigor of development', and 30% in the rest. Recommendations related to Korean medicine treatments such as on manual therapy related to Chuna were given in 6 CPGs, 4 for acupuncture, 1 for Qigong and 1 for Taping. Conclusions The 6 CPGs were developed up to now through a systematic development methodology and there were many recommendations for Korean medical treatments related to manual (Chuna) treatment, acupuncture and Qigong. However, the evidence for the side effects and risk factors of Korean medical treatments was scantly reflected in CPGs. Therefore, it is considered that balanced CPG with benefits and risks should be developed, covering Korean medical diagnosis, treatment and prognosis.