• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.13-34
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• 2012

The Paper Explores The Problem Of Recommending Talks To Attend At International Research Conferences. When Researchers Participate In Conferences, Finding Interesting Talks To Attend Is A Real Challenge. Given That Several Presentation Sessions And Social Activities Are Typically Held At A Time, And There Is Little Time To Analyze All Alternatives, It Is Easy To Miss Important Talks. In Addition, Compared With Recommendations Of Products Such As Movies, Books, Music, Etc. The Recipients Of Talk Recommendations (i.e. Conference Attendees) Already Formed Their Own Research Community On The Center Of The Conference Topics. Hence, Recommending Conference Talks Contains Highly Social Context. This Study Suggests That This Domain Would Be Suitable For Social Network-Based Recommendations. In Order To Find Out The Most Effective Recommendation Approach, Three Sources Of Information Were Explored For Talk Recommendation-Whateach Talk Is About (Content), Who Scheduled The Talks (Collaborative), And How The Users Are Connected Socially (Social). Using These Three Sources Of Information, This Paper Examined Several Direct And Hybrid Recommendation Algorithms To Help Users Find Interesting Talks More Easily. Using A Dataset Of A Conference Scheduling System, Conference Navigator, Multiple Approaches Ranging From Classic Content-Based And Collaborative Filtering Recommendations To Social Network-Based Recommendations Were Compared. As The Result, For Cold-Start Users Who Have Insufficient Number Of Items To Express Their Preferences, The Recommendations Based On Their Social Networks Generated The Best Suggestions.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.85-93
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• 2011

The music education improves the creative talent, social skills and academic achievement of the students. For the efficient music education, the learner centered study is highly recommended rather than the passive education, which supports self-control in selecting teaching materials, learning patterns and speed. For the successful self learning, it is requested to develop the collaborative educational learning tools, especially electronic collaborators such as H/W and S/W. Though there exist many commercialized electronic instruments and the PC MIDI based softwares, these tools have some limits and problems for the primary student to learn playing the musical instrument by himself. In this paper, we propose a supporting tool implementation method using an electronic keyboard instrument with USB Interface and PC-based software to help the primary student to learn playing the musical instrument. We implemented an electronic keyboard instrument module compactly and at low cost using a PIC18F4550 MCU. PC based software was developed to edit musical score, process the MIDI information, and interact with the electronic keyboard instrument module. This tool can offer a similar keyboard instrument environment and can be incorporated with self learning contents.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.51-54
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• 2006

본 논문에서는 각 사용자들의 취향에 맞는 음악을 추천하는 개인화된 음악 추천 시스템을 소개한다. 추천 시스템이란 사용자의 선호도를 분석하고 아이템들에 대한 사용자의 선호도를 예측하여 영화, 음악, 기사, 책, 웹 페이지 등과 같은 아이템들을 추천하는 시스템을 말한다. 추천 시스템들에서 가장 많이 사용하고 있는 협동적 추천 방식은 선호도 데이터를 기반으로 유사한 사용자들을 찾고, 유사 사용자들의 선호도를 기반으로 예측을 수행하는 것으로서, 여러 장점들이 있으나 희소성(sparsity) 문제와 확장성(scalability) 문제에 대해 취약점을 가지고 있다. 아이템들의 전체 수에 비해 매우 적은 수의 아이템 선호도 데이터만 존재한다면 사용자들의 유사도를 계산하기가 어려우며, 또한 사용자의 수가 늘어날수록 유사도 계산에 걸리는 시간이 급격하게 늘어남으로써 수백만 사용자가 있는 웹 사이트 등에서 실시간 추천을 수행하기 어렵다. 본 논문에서 소개하는 음악 추천 시스템은 이러한 문제점들을 해결하기 위해 그래프 기반 협동적 여과 기법을 사용한다. 그래프 기반 협동적 여과 기법은 기존의 협동적 여과 기법들과 달리 아이템들 사이의 연관관계를 그래프 모델로 표현하고 저장함으로써 묵시적인 선호도 정보들을 누적하여 희소성 문제를 해결하고, 추천 아이템을 선정하는데 필요한 계산 시간을 크게 단축하여 대규모 데이터에서 실시간 추천을 가능하게 한다는 장점이 있다.

• Cross-Cultural Studies
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• v.42
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• pp.241-260
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• 2016

The purpose of this article is to represent the types of advertising in the modern Russian language as 'Play of Words' (игра слов). The causal reason for this phenomenon is studied from the result of certain characteristics of advertising. The definition and characteristics of the language of the advertisement are analyzed in achieving the goal, as these factors reveal how language is used to maximize the effectiveness of the advertising. Academic research is needed in the collaborative fields of linguistics, psychology, economics, sociology, marketing, literature, art, and music. Modern advertisement is mixed with semiotic objects that consist of display, sound, and texts. While this study is not complete, the acknowledgement of the phenomenon 'Play of Words' between the creators of advertising and the consumer is undeniable. On one hand, advertising is recognized by linguists as the main factor that destroys the literary language. It represents the distortion of a standard language norm, as opposed to formal linguistic means used in advertising. In this research, we pay attention to the frequent use of foreign language borrowings and incorrect representation of foreign words, slang and jargon, that occur in misspelled usage of literary norms. The features that are revealed in this article are helpful to understand the purpose of advertising.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.115-126
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• 2023

In various fields such as art, design, music, film, sports, games, and fashion, NFTs (Non-Fungible Tokens) are creating new economic value through trading platforms dedicated to NFT art and content. In this article, I analyze the current state of blockchain technology and NFT art content in the context of an expanding market for blockchain-based NFT art content in the metaverse. I also propose several tasks based on the economic and industrial logic of technological innovation. The first task proposed is to integrate cultural arts on blockchain, metaverse, and NFT platforms through digital innovation, instead of separating or distinguishing between creative production and consumption. Before the COVID-19 pandemic, there was a clear separation between creators and consumers. However, with the rise of Web 3.0 platforms, any user can now create and own their own content. Therefore, it is important to promote a collaborative and integrated approach to cultural arts production and consumption in the blockchain and metaverse ecosystem. The second task proposed is to align the legal framework with blockchain-based technological innovation. The enactment and revision of relevant laws should focus on promoting the development of the NFT trading platform ecosystem, rather than merely regulating it for user protection. As blockchain-based technology continues to evolve, it is important that legal systems adapt to support and promote innovation in the space. This shift in focus can help create a more conducive environment for the growth of blockchain-based NFT platforms. The third task proposed is to integrate education on digital arts, including metaverse and NFT art contents, into the current curriculum. This education should focus on convergence and consilience, rather than merely mixing together humanities, technology, and arts. By integrating digital arts education into the curriculum, students can gain a more comprehensive understanding of the potential of blockchain-based technologies and NFT art. This article examines the digital technological innovation such as blockchain, metaverse, and NFT from an economic and industrial point of view. As a limitation of this research, the critical mind such as philosophical thinking or social criticism on technological innovation is left as a future task.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.