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Today, the world is under siege from various kinds of fake news ranging from politics to COVID-19. Thus, many scholars have been researching automatic fake news detection based on artificial intelligence and machine learning (AI/ML) to prevent the spread of fake news. The mainstream research on detecting fake news so far has been text-based detection approaches, but they have inherent limitations such as the difficulty of short text processing and language dependency. Thus, as an alternative to the text-based approach, the context-based approach is emerging. The most common context-based approach the use of distributors’ network information in social media. However, such information is difficult to obtain, and only propagation within a single social media can be traced. Under this background, we propose the use of composition pattern of web links containing news content as a new source of information for fake news detection. To properly vectorize the composition pattern of web links, this study proposes a novel embedding technique, which is called link2vec, an extension of word2vec. To test the effectiveness and language independency of our link2vec-based model, we applied it to two real-world fake news datasets in different languages (English and Korean). As comparison models, we adopted the conventional text-based model and a hybrid model that combined text and whitelist-based link information proposed by a prior study. Results revealed that in the datasets in two languages, the link2vec-based detection models outperformed all the comparison models with statistical significance. Our research is expected to contribute to suggesting a completely new path for effective fake news detection.
Jae-Seung Shim; YunJu Lee; Hyunchul Ahn. A Link2vec-based Fake News Detection Model using Web Search Results. Expert Systems with Applications 2021, 184, 115491 .
AMA StyleJae-Seung Shim, YunJu Lee, Hyunchul Ahn. A Link2vec-based Fake News Detection Model using Web Search Results. Expert Systems with Applications. 2021; 184 ():115491.
Chicago/Turabian StyleJae-Seung Shim; YunJu Lee; Hyunchul Ahn. 2021. "A Link2vec-based Fake News Detection Model using Web Search Results." Expert Systems with Applications 184, no. : 115491.
To enhance the sustainability of business operations, enterprises have interests in enterprise resource planning (ERP) transitions from an existing on-premise method to a cloud-based system. This study conducts a comprehensive analysis using the technology-organization-environment, diffusion of innovation, and the model of innovation resistance frameworks. The empirical analysis shows that the factors of organizational culture, regulatory environment, relative advantage, trialability, and vendor lock-in all had a significant influence on the intention to adopt cloud-based ERP, while information and communications technology skill, complexity, observability, data security, and customization had no significant influence on the intention to adopt cloud-based ERP. This study’s findings provide meaningful guidance for companies that want to adopt cloud-based ERP, governments that support enterprise digitalization, and vendors who sell cloud-based ERP systems.
Byungchan Ahn; Hyunchul Ahn. Factors Affecting Intention to Adopt Cloud-Based ERP from a Comprehensive Approach. Sustainability 2020, 12, 6426 .
AMA StyleByungchan Ahn, Hyunchul Ahn. Factors Affecting Intention to Adopt Cloud-Based ERP from a Comprehensive Approach. Sustainability. 2020; 12 (16):6426.
Chicago/Turabian StyleByungchan Ahn; Hyunchul Ahn. 2020. "Factors Affecting Intention to Adopt Cloud-Based ERP from a Comprehensive Approach." Sustainability 12, no. 16: 6426.
Enterprises have implemented enterprise resource planning (ERP) systems as a strategic vehicle to gain a competitive edge. However, such ERP systems do not always guarantee successful results. While ERP systems may provide an organization with numerous benefits, they can also destroy a business if not successfully adopted, owing to enormous investment losses coupled with low business efficiency. To explore a way to reverse this situation, we examine how organizational citizenship behavior influences the successful management of ERP systems. Moreover, the mediating role of absorptive capacity in this relationship is investigated. The empirical analysis results, based on 188 surveyed organizations in Korea, reveal a partial mediating role of absorptive capacity on the relationship between organizational citizenship behavior and ERP usage performance. The findings of the study shed light on the ways of how the companies that adopt ERP systems to facilitate ERP usage and to gain business sustainability.
Kee-Young Kwahk; Sung-Byung Yang; Hyunchul Ahn. How Organizational Citizenship Behavior Affects ERP Usage Performance: The Mediating Effect of Absorptive Capacity. Sustainability 2020, 12, 4462 .
AMA StyleKee-Young Kwahk, Sung-Byung Yang, Hyunchul Ahn. How Organizational Citizenship Behavior Affects ERP Usage Performance: The Mediating Effect of Absorptive Capacity. Sustainability. 2020; 12 (11):4462.
Chicago/Turabian StyleKee-Young Kwahk; Sung-Byung Yang; Hyunchul Ahn. 2020. "How Organizational Citizenship Behavior Affects ERP Usage Performance: The Mediating Effect of Absorptive Capacity." Sustainability 12, no. 11: 4462.
Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.
Kyoung-Jae Kim; Kichun Lee; Hyunchul Ahn. Predicting Corporate Financial Sustainability Using Novel Business Analytics. Sustainability 2018, 11, 64 .
AMA StyleKyoung-Jae Kim, Kichun Lee, Hyunchul Ahn. Predicting Corporate Financial Sustainability Using Novel Business Analytics. Sustainability. 2018; 11 (1):64.
Chicago/Turabian StyleKyoung-Jae Kim; Kichun Lee; Hyunchul Ahn. 2018. "Predicting Corporate Financial Sustainability Using Novel Business Analytics." Sustainability 11, no. 1: 64.
Trust is the key ingredient for sustainable transactions. In the concept of trust, the trustor trusts the trustees. In e-commerce, the trustor is the buyer and the trustees are the intermediaries and the seller. Intermediaries provide the web-based infrastructure that enables buyers and sellers to make transactions. Trust is the buyer’s judgment and comprises two distinct concepts; both trust and distrust reside in the trustor. The purpose of this study was to examine the complicated effects of trust and distrust on a buyer’s purchase intentions. Previous studies have provided theoretical frameworks illustrating co-existent trust and distrust, trust transfers from one to another, and trust in buyer-intermediary-seller relationships. Based on these frameworks, this study (i) presented a holistic model that contained the judgment of buyers resulting in trust or distrust in the intermediary and the seller; (ii) investigated trust and distrust transfer from the intermediary to the seller, and (iii) explored the effects of various antecedents that affect trust and distrust. To validate the proposed model, we employed Partial Least Squares (PLS). A summary of key findings are as follows. First, buyer’s trust in an intermediary positively affected his or her trust in the seller, positively influencing purchase intention. In other words, we found the trust transfer from an intermediary to its seller. Second, distrust in an intermediary directly impacted on the buyer’s perceived risk, negatively influencing his or her purchase intentions. Third, structural assurance and perceived website quality of an intermediary gave a positive impact on buyer’s trust in the intermediary. The results of this study shed light on the necessity of managing both trust and distrust to facilitate sales in e-commerce.
Suk-Joo Lee; Cheolhwi Ahn; Kelly Minjung Song; Hyunchul Ahn. Trust and Distrust in E-Commerce. Sustainability 2018, 10, 1015 .
AMA StyleSuk-Joo Lee, Cheolhwi Ahn, Kelly Minjung Song, Hyunchul Ahn. Trust and Distrust in E-Commerce. Sustainability. 2018; 10 (4):1015.
Chicago/Turabian StyleSuk-Joo Lee; Cheolhwi Ahn; Kelly Minjung Song; Hyunchul Ahn. 2018. "Trust and Distrust in E-Commerce." Sustainability 10, no. 4: 1015.
Kee-Young Kwahk; Hyunchul Ahn; Young U. Ryu. Understanding mandatory IS use behavior: How outcome expectations affect conative IS use. International Journal of Information Management 2018, 38, 64 -76.
AMA StyleKee-Young Kwahk, Hyunchul Ahn, Young U. Ryu. Understanding mandatory IS use behavior: How outcome expectations affect conative IS use. International Journal of Information Management. 2018; 38 (1):64-76.
Chicago/Turabian StyleKee-Young Kwahk; Hyunchul Ahn; Young U. Ryu. 2018. "Understanding mandatory IS use behavior: How outcome expectations affect conative IS use." International Journal of Information Management 38, no. 1: 64-76.
The Effect of Trustworthiness on Purchase Intention in Open Markets: Focusing on Trust and Distrust Open Market;Trust;Distrust;Trustworthiness; Purpose This study investigates the effects of trust and distrust on intention to purchase in open market, based on the idea that trust and distrust can co-exist. Specifically, this study approached the effects of trust and distrust of the open market on the intention to purchase from a two-dimensional perspective, and examined trustworthiness as the antecedents of trust and distrust. Design/Methodology/Approach In this study, we conducted a questionnaire survey on consumers who have actually purchased a product from open markets in Korea for two months. As a result, 141 users are chosen for the sample. We apply PLS (Partial Least Squares) structural equation modeling (SEM) to verify our theoretical model using the software application SmartPLS 3.0. Findings First, trust in open market positively affects intention to purchase, whereas distrust in open market negatively affects intention to purchase. Second, the three antecedents of trust (i.e. three factors constituting trustworthiness such as ability, benevolence and integrity) affect trust in open market. Third, integrity negatively affects distrust in open market. Since integrity plays an important role in building both trust and distrust in open market, the operators of open market should pay attention to managing their integrity.
Seul Bi Choi; Hyunchul Ahn; Kee-Young Kwahk. The Effect of Trustworthiness on Purchase Intention in Open Markets: Focusing on Trust and Distrust. The Journal of Information Systems 2017, 26, 171 -188.
AMA StyleSeul Bi Choi, Hyunchul Ahn, Kee-Young Kwahk. The Effect of Trustworthiness on Purchase Intention in Open Markets: Focusing on Trust and Distrust. The Journal of Information Systems. 2017; 26 (1):171-188.
Chicago/Turabian StyleSeul Bi Choi; Hyunchul Ahn; Kee-Young Kwahk. 2017. "The Effect of Trustworthiness on Purchase Intention in Open Markets: Focusing on Trust and Distrust." The Journal of Information Systems 26, no. 1: 171-188.
Kyoung-Jae Kim; Hyunchul Ahn. Recommender systems using cluster-indexing collaborative filtering and social data analytics. International Journal of Production Research 2017, 55, 5037 -5049.
AMA StyleKyoung-Jae Kim, Hyunchul Ahn. Recommender systems using cluster-indexing collaborative filtering and social data analytics. International Journal of Production Research. 2017; 55 (17):5037-5049.
Chicago/Turabian StyleKyoung-Jae Kim; Hyunchul Ahn. 2017. "Recommender systems using cluster-indexing collaborative filtering and social data analytics." International Journal of Production Research 55, no. 17: 5037-5049.
Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users Recommender system;Collaborative filtering;Social network analysis;Centrality;Trust relationship network; Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.
Seulbi Choi; Kee-Young Kwahk; Hyunchul Ahn. Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users. Journal of Intelligence and Information Systems 2016, 22, 113 -127.
AMA StyleSeulbi Choi, Kee-Young Kwahk, Hyunchul Ahn. Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users. Journal of Intelligence and Information Systems. 2016; 22 (3):113-127.
Chicago/Turabian StyleSeulbi Choi; Kee-Young Kwahk; Hyunchul Ahn. 2016. "Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users." Journal of Intelligence and Information Systems 22, no. 3: 113-127.
Hyunchul Ahn; Hyoung-Yong Lee. A Study on the Social Commerce in Smartphone Environment. Journal of the Korea society of IT services 2015, 14, 145 -158.
AMA StyleHyunchul Ahn, Hyoung-Yong Lee. A Study on the Social Commerce in Smartphone Environment. Journal of the Korea society of IT services. 2015; 14 (1):145-158.
Chicago/Turabian StyleHyunchul Ahn; Hyoung-Yong Lee. 2015. "A Study on the Social Commerce in Smartphone Environment." Journal of the Korea society of IT services 14, no. 1: 145-158.
Tuanhung Dao; Hyunchul Ahn. An Optimized Combination of π-fuzzy Logic and Support Vector Machine for Stock Market Prediction. Journal of Intelligence and Information Systems 2014, 20, 43 -58.
AMA StyleTuanhung Dao, Hyunchul Ahn. An Optimized Combination of π-fuzzy Logic and Support Vector Machine for Stock Market Prediction. Journal of Intelligence and Information Systems. 2014; 20 (4):43-58.
Chicago/Turabian StyleTuanhung Dao; Hyunchul Ahn. 2014. "An Optimized Combination of π-fuzzy Logic and Support Vector Machine for Stock Market Prediction." Journal of Intelligence and Information Systems 20, no. 4: 43-58.
The demand for extracting keywords related to national issues from various sources and using them to retrieve R&D information has increased rapidly recently. In order to satisfy this demand, three methodologies are proposed in this study: a hybrid methodology for extracting and integrating national issue keywords, a methodology for packaging R&D information that corresponds to national issues, and a methodology for generating an associative issue network related to relevant R&D information. Data analysis techniques, such as text mining, social network analysis, and association rules mining, are utilized to establish these methodologies.
Namgyu Kim; William Wong Xiu Shun; Jieun Kim; Kee-Young Kwahk; Seungryul Jeong; Hyunchul Ahn. Constructing an Issue Network from the Perspective of Common R&D Keywords. 2014 IEEE International Congress on Big Data 2014, 772 -773.
AMA StyleNamgyu Kim, William Wong Xiu Shun, Jieun Kim, Kee-Young Kwahk, Seungryul Jeong, Hyunchul Ahn. Constructing an Issue Network from the Perspective of Common R&D Keywords. 2014 IEEE International Congress on Big Data. 2014; ():772-773.
Chicago/Turabian StyleNamgyu Kim; William Wong Xiu Shun; Jieun Kim; Kee-Young Kwahk; Seungryul Jeong; Hyunchul Ahn. 2014. "Constructing an Issue Network from the Perspective of Common R&D Keywords." 2014 IEEE International Congress on Big Data , no. : 772-773.
Kichun Lee; So Yun Choi; Jae Kyeong Kim; Hyunchul Ahn. Multimodal Emotional State Estimation Model for Implementation of Intelligent Exhibition Services. Journal of Intelligence and Information Systems 2014, 20, 1 -14.
AMA StyleKichun Lee, So Yun Choi, Jae Kyeong Kim, Hyunchul Ahn. Multimodal Emotional State Estimation Model for Implementation of Intelligent Exhibition Services. Journal of Intelligence and Information Systems. 2014; 20 (1):1-14.
Chicago/Turabian StyleKichun Lee; So Yun Choi; Jae Kyeong Kim; Hyunchul Ahn. 2014. "Multimodal Emotional State Estimation Model for Implementation of Intelligent Exhibition Services." Journal of Intelligence and Information Systems 20, no. 1: 1-14.
Hyoung-Yong Lee; Hyunchul Ahn; Heung Kee Kim; Jongwon Lee. Comparative Analysis of Trust in Online Communities. Procedia Computer Science 2014, 31, 1140 -1149.
AMA StyleHyoung-Yong Lee, Hyunchul Ahn, Heung Kee Kim, Jongwon Lee. Comparative Analysis of Trust in Online Communities. Procedia Computer Science. 2014; 31 ():1140-1149.
Chicago/Turabian StyleHyoung-Yong Lee; Hyunchul Ahn; Heung Kee Kim; Jongwon Lee. 2014. "Comparative Analysis of Trust in Online Communities." Procedia Computer Science 31, no. : 1140-1149.
Seung Hee Ho; Hyunchul Ahn; Na Young Kim; So Yeon Yu; Ye Soon Kim; Jong Wook Won; Han Joon Kim; Sung Youl Cho. The development of a decision support system of vocational counseling for people with disabilities. Studies in health technology and informatics 2013, 192, 1 .
AMA StyleSeung Hee Ho, Hyunchul Ahn, Na Young Kim, So Yeon Yu, Ye Soon Kim, Jong Wook Won, Han Joon Kim, Sung Youl Cho. The development of a decision support system of vocational counseling for people with disabilities. Studies in health technology and informatics. 2013; 192 ():1.
Chicago/Turabian StyleSeung Hee Ho; Hyunchul Ahn; Na Young Kim; So Yeon Yu; Ye Soon Kim; Jong Wook Won; Han Joon Kim; Sung Youl Cho. 2013. "The development of a decision support system of vocational counseling for people with disabilities." Studies in health technology and informatics 192, no. : 1.
Predicting corporate credit-rating using statistical and artificial intelligence (AI) techniques has received considerable research attention in the literature. In recent years, multi-class support vector machines (MSVMs) have become a very appealing machine-learning approach due to their good performance. Until now, researchers have proposed a variety of techniques for adapting support vector machines (SVMs) to multi-class classification, since SVMs were originally devised for binary classification. However, most of them have only focused on classifying samples into nominal categories; thus, the unique characteristic of credit-rating - ordinality - seldom has been considered in the proposed approaches. This study proposes a new type of MSVM classifier (named OMSVM) that is designed to extend the binary SVMs by applying an ordinal pairwise partitioning (OPP) strategy. Our model can efficiently and effectively handle multiple ordinal classes. To validate OMSVM, we applied it to a real-world case of bond rating. We compared the results of our model with those of conventional MSVM approaches and other AI techniques including MDA, MLOGIT, CBR, and ANNs. The results showed that our proposed model improves the performance of classification in comparison to other typical multi-class classification techniques and uses fewer computational resources.
Kyoung-Jae Kim; Hyunchul Ahn. A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research 2012, 39, 1800 -1811.
AMA StyleKyoung-Jae Kim, Hyunchul Ahn. A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research. 2012; 39 (8):1800-1811.
Chicago/Turabian StyleKyoung-Jae Kim; Hyunchul Ahn. 2012. "A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach." Computers & Operations Research 39, no. 8: 1800-1811.
Recommender systems are the efficient and most used tools that prevail over the information overload problem, provide users with the most appropriate content by considering their personal preferences (mostly, ratings). In addition to these preferences, taking into account the interaction context of users will improve the relevancy of the recommendation process. However, only a few prior studies have tried to adopt context-awareness to the recommendation model. Although a number of studies have developed recommendation models using collaborative filtering (CF), few of them have tried to adopt both CF and other artificial intelligence techniques, such as genetic algorithm (GA), as a tool to improve recommendation results. In this paper, we propose a new recommendation model, which we termed Context-Aware Collaborative Filtering using genetic algorithm (CACF-GA), for location-based advertising (LBA) based on both user's preferences and interaction's context. We first defined discrete contexts, and then applied the concept of ''context similarity'' to conventional CF to create the context-aware recommendation model. The context similarity between two contexts is designed to be optimized using GA. We collect real-world data from mobile users, build a LBA recommendation model using CACF-GA, and then perform an empirical test to validate the usefulness of CACF-GA. Experiments show our proposed model provides the most accurate prediction results compared to comparative ones.
Tuan Hung Dao; Seung Ryul Jeong; Hyunchul Ahn. A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach. Expert Systems with Applications 2012, 39, 3731 -3739.
AMA StyleTuan Hung Dao, Seung Ryul Jeong, Hyunchul Ahn. A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach. Expert Systems with Applications. 2012; 39 (3):3731-3739.
Chicago/Turabian StyleTuan Hung Dao; Seung Ryul Jeong; Hyunchul Ahn. 2012. "A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach." Expert Systems with Applications 39, no. 3: 3731-3739.
Hee-Joo Kang; Seung-Ryul Jeong; Hyun-Chul Ahn. A Study on the Effect of the Fit between the Type of Business Process Change and Organizational Culture on the Business Process Change Success. The Journal of Information Systems 2011, 20, 49 -72.
AMA StyleHee-Joo Kang, Seung-Ryul Jeong, Hyun-Chul Ahn. A Study on the Effect of the Fit between the Type of Business Process Change and Organizational Culture on the Business Process Change Success. The Journal of Information Systems. 2011; 20 (4):49-72.
Chicago/Turabian StyleHee-Joo Kang; Seung-Ryul Jeong; Hyun-Chul Ahn. 2011. "A Study on the Effect of the Fit between the Type of Business Process Change and Organizational Culture on the Business Process Change Success." The Journal of Information Systems 20, no. 4: 49-72.
Collaborative filtering (CF) is regarded as one of the most popular recommendation methods. However, CF has some significant weaknesses, such as problems of sparsity and scalability. Sparsity causes inaccuracy in the formation of neighbors with similar interests, and scalability prevents CF from scaling up with increases in the number of users and/or items. To mitigate these problems, this study proposes a hybrid CF and genetic algorithm (GA) model. GAs are widely believed to be effective on NP-complete global optimization problems, and they can provide good suboptimal solutions in a reasonable amount of time. In this study, the GA searches for relevant users and items from a user-item matrix not only to condense the matrix but also to improve the prediction accuracy. The reduced user-item matrix may reduce the sparsity problem by increasing the likelihood that different customers rate common items. It also shrinks the search space for CF, which ameliorates the scalability problem. Experimental results show that the proposed model improves performance and speed compared to the typical CF model.
Kyoung-Jae Kim; Hyunchul Ahn. Collaborative Filtering with a User-Item Matrix Reduction Technique. International Journal of Electronic Commerce 2011, 16, 107 -128.
AMA StyleKyoung-Jae Kim, Hyunchul Ahn. Collaborative Filtering with a User-Item Matrix Reduction Technique. International Journal of Electronic Commerce. 2011; 16 (1):107-128.
Chicago/Turabian StyleKyoung-Jae Kim; Hyunchul Ahn. 2011. "Collaborative Filtering with a User-Item Matrix Reduction Technique." International Journal of Electronic Commerce 16, no. 1: 107-128.
Following deregulation and liberalization of the mobile telecommunications sector, the mobile telecommunication market is becoming increasingly saturated. Mobile operators are confronted with a sluggish user growth rate and a fall in the average revenue per user (ARPU). Mobile value-added services (VAS) are expected to form mobile operators’ strategy to compensate dwindling revenues. However, not only is it difficult to analyze which types of customers are willing to use VAS, but it is equally difficult to understand the diversified customer preferences on VAS. This study proposes an integrated scoring model that includes multiple classification models. It analyzes and distinguishes the potential prospects of VAS. We applied our model to the case of a mobile operator in Korea to validate its usefulness. We explored the prospects for melody bells for that company. We found that our proposed scoring model produces better results than other comparative models.
Hyunchul Ahn; Jae Joo Ahn; Hyun Woo Byun; Kyong Joo Oh. A novel customer scoring model to encourage the use of mobile value added services. Expert Systems with Applications 2011, 38, 11693 -11700.
AMA StyleHyunchul Ahn, Jae Joo Ahn, Hyun Woo Byun, Kyong Joo Oh. A novel customer scoring model to encourage the use of mobile value added services. Expert Systems with Applications. 2011; 38 (9):11693-11700.
Chicago/Turabian StyleHyunchul Ahn; Jae Joo Ahn; Hyun Woo Byun; Kyong Joo Oh. 2011. "A novel customer scoring model to encourage the use of mobile value added services." Expert Systems with Applications 38, no. 9: 11693-11700.