• Information Systems Review
• /
• v.19 no.2
• /
• pp.1-19
• /
• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• Korean Journal of Plant Resources
• /
• v.37 no.4
• /
• pp.411-422
• /
• 2024

Naked oat is particularly vulnerable to cold weather among cereal crops, and frequent lodging due to their long culm length. Also it is difficult to cultivate in double cropping system with rice because of late maturity in Korea. In this study, we charaterized 71 naked oat germplasms including two control varieties with agronomic traits, to select superior resources and use them for the improvement of exsiting varieties. The maturity date was May 26th for 'Joyang' and June 1st for 'Daeyang', and there were 5 resources including IT302003, which was earlier than Daeyang. In the case of culm length, the distribution ranged from 62 to 124 cm, and there were 13 resources, including IT209241, that were shorter than 'Joyang' (84 cm) and 'Daeyang' (83 cm). In correlation analysis of agronomic traits, the highest positive correlation (r=0.89) was observed between days to heading and days to maturity. In addition, culm length and spike length (r=0.72) and days to heading (r=0.50) showed a positive correlation. However, Barley yellow dwarf virus(BYDV) and grain yield (r=-0.30), lodging tolerance and liter weight (r=-0.37) showed a negative correlation. Cluster analysis, resources with similar traits such as liter weight, husk ratio, days to heading, and days to maturing were subclassified. Through this analysis, 6 resources, including IT302002 (X345-1-B4-20-1), which had short culm length, early maturity, and cold tolerance compared to 'Joyang' and 'Daeyang', were selected. Among the selected excellent resources, the avenanthramide content of the four resources was IT302002 (325.18 ㎍/g), K000010 (557.3㎍/g), K253299 (447.33 ㎍/g), and K253301 (440.49 ㎍/g), respectively, which was higher than 'Daeyang' (301.97 ㎍/g). These excellent resources will be used as breeding materials for the development of new varieties in the future.

• Journal of the Korean Institute of Traditional Landscape Architecture
• /
• v.32 no.1
• /
• pp.63-73
• /
• 2014

This study was intended to analyze the correlation between the builder's intentions and the colors that are revealed through the analysis of the manifestation of symbolic colors as the signs and symbols that are shown in the traditional space of Soshaewon as one of the representative retreating villa gardens of Chosun Dynasty and the interpretation of the meanings. The research on the traditional colors and the intended background of Soshaewon and so on was conducted by examining the relative literature and the contents as the objects for the empirical analysis were the scenic beauties and spatial elemnts that are shown in the plan of Soshewon and the poems about Soshewon 48 scenes as well as the contents of the poems related to Five Primary Colors that are used based on "the Ideology of Five Elements of the Positive and Negative". Espectially, this study was conducted by dividing the spatial elements shown in Soswaewon broadly into the visual, synesthetic and symbolic & cognitive spaces in order to classify the locations and directions of the spaces and natural objects as the visual space, the seasons, the points of times and the five sensory organs as the synesthetic space and the "Four Moral Begginings" & the "Seven Emotions" of New Confucianism as the symbolic and cognitive space and by analyzing if there are any correlation between the decided intentions and the spatial meanings that are revealed by analyzing the system of five primary colors which are used in each spatial element.7) As a result from the analysis of the colors that are used based on the three spatial components of Soshaewon, it was found that one of Five Primary Colors were used 74 times in total. First, in the visual spatial component, one of the colors was used 18 times in total including 2 times of blue, 1 time of red, 6 times of yellow, 4 times of white and 5 times of black. The fact that yellow symbolizing the source of everything and white symoblizing cleanness and pureness are used relatively more often than blue and red symbolizing nature and life respectively can be interpreted that the builder, Mr. San-bo Yang's intention to build Soshaewon is related to the meaning symbolizing the pure will of the builder who wanted to seclude himself from society rather than to give priority to enjoying nature. Second, in the synesthetic spatial component which are selected from the 48 scenes of Soshaewon, one of Five Primary Colors was used 38 times in total including 11 times of blue, 8 times of red, 4 times of yellow, 5 times of white and 10 times of black. Since blue and red symbolizing nature and life respectively are used more often than yellow and black symbolizing the source of the world and pureness & knowledge respectively, it was interpreted that the builder means to be faithful to the unique basic functions of the retreating villa with which the builder wanted to enjoy his passion for enjoying nature and exploring knowledge even though the builder built the villa garden for the purpose of keeping himself pure while being unstained by the world. Finally, it was analyzed that one of Five Primary Colors was used 18 times in total including 4 times of blue, 5 times of red and 9 times of white in the symbolic and cognitive spatial components of the 48 scenes. Since it was found that white symbolizing the builder's political pureness is used at the same frequency as blue and red symbolizing nature and life respectively are used as a result from the analysis, it was interpreted that the intention of creating Soshaewon was emotionally revealed at the same percentage in the aspects of his political pureness and enjoyment of nature through his poems. Especially, as a result from such 3 kinds of analyses regarding to the frequency of using Five Primary Colors, it was found that the number of times using Five Primary Colors in the synesthetic spatial component was 38 as the highest percentage, around 51% followed by each 18 times as the24.5% respectively in the visual and cognitive & symbolic spatial spaces. Based on such results, it can be interpreted that the builder, Mr. San-bo Yang's intention of creating Soshaewon sympolizes that his intention for enjoying the basic nature and the vitality of nature within the retreating villa garden is hidden at the same proportion as his intention for keeping himself pure from the unrightful world. It was intended to reveal the traditional colors which are hidden in a traditional retreating villa by analyzing the correlation between the symbolic meanings and the spatial components of Soshaewon based on the Five Primary Colors which are originated from the "Ideology of the Five Natural Elements of the Positive and Negative", but it is thought that it is possible to explore whether such a theory can be generally applied to other kinds of retreating villa gardens or not through a study on other retreating villa gardens as other examples in which the builder's apparent pure intentions, such as enjoyment of nature and persuit of study, etc., unlike Soshaewon are hidden.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.155-175
• /
• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.111-126
• /
• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Sijohaknonchong
• /
• v.30
• /
• pp.263-300
• /
• 2009

Adoration for nature constitutes one of the primary subjects that literature has tackled since the origin of human history. Nature expressed through a poet's subjective imagination is the internalized and reorganized nature. This study examines the view on nature enacted in Ch'o-Jung's three-verse poems (sijo) in light of the traditional views on nature implicated in the ancient three-verse poems (koshijo), which is in line with the long-established Oriental view on nature. To dignitaris(sadaebu) in the Chosun Dynasty, nature appeared as the idealistic subject for moral culture ($shims{\breve{o}}ngsuyang$), which also becomes the literary space where the purity and justice of the world view of Neo-Confucianism(Sungrihak) is contained in the form of the three-verse poem, the lyrical poetic space where the "I" is united with nature by way of "enjoying of wind and moon"($umpungnongw{\breve{o}}i$) and "living in quiet retiremen"($yuyuchaj{\breve{o}}k$), and the object for the poetical perception of the surrounding world. Ch'o-Jung' s three-verse odes are found in Reed pipe ($Ch'oj{\breve{o}}k$), Sixty Five Pieces of Three-Verse Odes (Samhaengshi-$yukshipopy{\breve{o}}n$), Autumn Fragrance ($Hyangginam{\check{u}}n-ga{\check{u}}l$), and The Words of Zelko va Tree ($N{\check{u}}tinamu{\check{u}}i-mal$). This study analyzes 212 pieces of Ch'o-Jung' s three-verse poems chosen from theses books. In Ch'o-Jung's poems, the traditional view on nature expressed in the ancient three-verse poems is rendered in such a way that metaphysical understanding of nature is indirectly transmitted through the objective correlatives found nature. Nature is no longer the object of straightforward utterance, but transformed, displaced, and removed: that way, nature gets objectified to form a complicated and multi-layered structure. In conclusion, the view on nature manifested in Ch'o-Jung's three-verse poems is based on traditional metaphysics. Second, nature is the object of lyrical nostalgia and adoration. Third, nature is imbued with the fundamental affection for parents. Fourth, nature is associated with organic life. Fifth, the nature in Ch'o-Jung's poems reveals the beauty of stillness endorsed in Lao-tse's and Chung-tze's philosophy. And last, nature is the agent for self-realization and meditation.

• Journal of Intelligence and Information Systems
• /
• v.23 no.3
• /
• pp.29-44
• /
• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
• /
• v.26 no.3
• /
• pp.149-169
• /
• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.177-193
• /
• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.27-42
• /
• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.