• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.475-486
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• 2010

Contents Recommender System predicts user's preferences towards contents, and then recommends highly-predicted contents to user. Digital Identifier plays its part in identifying abstract works or digital contents in digital network environment. Digital Identifier could be effectively used in content-based filtering and collaborative filtering that are mainly used in Contents Recommender Systems. Therefore, this paper proposes an improvement of UCI metadata and resolution service for effective use of UCI in massive contents recommender systems. UCI metadata is expanded by adding elements such as abstract, keyword, genre, age, rate and review. Resolution service allows the operation systems to collect user preference for content by including input part of preference in a result page. This paper also designs and implements an improved UCI operation system and shows that the proposed improvement of UCI metadata and resolution service could be used for massive contents recommendation.

• Archives of design research
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• v.16 no.2
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• pp.23-34
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• 2003

The need for personalized web site design has been increased these days. Current approach for personalized web site design is easily applied to web site with their cost-effective feature, but is hard to provide a more refined personalized service due to its lack of accumulation of user data. In this study, the design recommender system is investigated as a more advanced method for web site design personalization. We provide an overview of current recommender systems, and then outlined a newly developed design recommender system, which employs collaborative filtering technique to provide tailored recommendation for users.

• Journal of KIISE
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• v.44 no.9
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• pp.908-917
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• 2017

As web service technologies are widely used, there have been many efforts to develop approaches for recommending appropriate web services to users in complex and dynamic service environments. In addition, for the effective development of service mashups, service recommender systems that are specialized for service composition have been developed. However, existing service recommender systems for service mashups are not effective at recommending services in a personalized manner that reflect developers' preferences. To deal with this issue, we propose an approach that recommends services based on the similarities between mashup developers who have developed similar service mashups. The proposed approach is then evaluated by using the mashup data retrieved from ProgrammableWeb. The evaluation results clearly show that the proposed approach is an effective way of improving service recommendations compared to the traditional user-based collaborative filtering algorithm.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.254-266
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• 2020

The purpose of this study is to analyze the influence of the type of recommender system on consumer's perceived benefit and privacy risk. The result showed that the perceived usefulness and intension to click was high in the order of Hybrid-filtering, Bestseller, and SNS-based system. Privacy concern was high in order of SNS-based system, Hybrid-filtering, and Bestseller. Moderating effects of perceived personalization on the type of recommender system and perceived usefulness were significant. Finally perceived usefulness had positive effect, and privacy concern had negative effect on consumer's intension to click. This study has significant implications for digital marketing bt comparing consumer responses according to the type of recommended service. The result of this study can be helpful for providing and developing future recommender service.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.181-199
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• 2010

As rapid progress of population aging and strong interest in health, the demand for new healthcare service is increasing. Until now healthcare service has provided post treatment by face-to-face manner. But according to related researches, proactive treatment is resulted to be more effective for preventing diseases. Particularly, the existing healthcare services have limitations in preventing and managing metabolic syndrome such a lifestyle disease, because the cause of metabolic syndrome is related to life habit. As the advent of ubiquitous technology, patients with the metabolic syndrome can improve life habit such as poor eating habits and physical inactivity without the constraints of time and space through u-healthcare service. Therefore, lots of researches for u-healthcare service focus on providing the personalized healthcare service for preventing and managing metabolic syndrome. For example, Kim et al.(2010) have proposed a healthcare model for providing the customized calories and rates of nutrition factors by analyzing the user's preference in foods. Lee et al.(2010) have suggested the customized diet recommendation service considering the basic information, vital signs, family history of diseases and food preferences to prevent and manage coronary heart disease. And, Kim and Han(2004) have demonstrated that the web-based nutrition counseling has effects on food intake and lipids of patients with hyperlipidemia. However, the existing researches for u-healthcare service focus on providing the predefined one-way u-healthcare service. Thus, users have a tendency to easily lose interest in improving life habit. To solve such a problem of u-healthcare service, this research suggests a u-healthcare recommender system which is based on collaborative filtering principle and social network. This research follows the principle of collaborative filtering, but preserves local networks (consisting of small group of similar neighbors) for target users to recommend context aware healthcare services. Our research is consisted of the following five steps. In the first step, user profile is created using the usage history data for improvement in life habit. And then, a set of users known as neighbors is formed by the degree of similarity between the users, which is calculated by Pearson correlation coefficient. In the second step, the target user obtains service information from his/her neighbors. In the third step, recommendation list of top-N service is generated for the target user. Making the list, we use the multi-filtering based on user's psychological context information and body mass index (BMI) information for the detailed recommendation. In the fourth step, the personal information, which is the history of the usage service, is updated when the target user uses the recommended service. In the final step, a social network is reformed to continually provide qualified recommendation. For example, the neighbors may be excluded from the social network if the target user doesn't like the recommendation list received from them. That is, this step updates each user's neighbors locally, so maintains the updated local neighbors always to give context aware recommendation in real time. The characteristics of our research as follows. First, we develop the u-healthcare recommender system for improving life habit such as poor eating habits and physical inactivity. Second, the proposed recommender system uses autonomous collaboration, which enables users to prevent dropping and not to lose user's interest in improving life habit. Third, the reformation of the social network is automated to maintain the quality of recommendation. Finally, this research has implemented a mobile prototype system using JAVA and Microsoft Access2007 to recommend the prescribed foods and exercises for chronic disease prevention, which are provided by A university medical center. This research intends to prevent diseases such as chronic illnesses and to improve user's lifestyle through providing context aware and personalized food and exercise services with the help of similar users'experience and knowledge. We expect that the user of this system can improve their life habit with the help of handheld mobile smart phone, because it uses autonomous collaboration to arouse interest in healthcare.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.15-28
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• 2015

As personal devices and pervasive technologies for interacting with networked objects continue to proliferate, there is an unprecedented world of scattered pieces of contextualized information available. However, the explosive growth and variety of information ironically lead users and service providers to make poor decision. In this situation, recommender systems may be a valuable alternative for dealing with these information overload. But they failed to utilize various types of contextual information. In this study, we suggest a methodology for context-aware recommender systems based on the concept of contextual boundary. First, as we suggest contextual boundary-based profiling which reflects contextual data with proper interpretation and structure, we attempt to solve complexity problem in context-aware recommender systems. Second, in neighbor formation with contextual information, our methodology can be expected to solve sparsity and cold-start problem in traditional recommender systems. Finally, we suggest a methodology about context support score-based recommendation generation. Consequently, our methodology can be first step for expanding application of researches on recommender systems. Moreover, as we suggest a flexible model with consideration of new technological development, it will show high performance regardless of their domains. Therefore, we expect that marketers or service providers can easily adopt according to their technical support.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.43-56
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• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• Journal of Broadcast Engineering
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• v.16 no.2
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• pp.237-246
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• 2011

In order to produce the variety of UCC, the recommendation service is required which considers the copyright of UCC producer discriminated from one for UCC consumers and the purpose of its production. The recommender system designed in this thesis enables UCC which is much similar to one UCC producer utilizes to be used with custom-made when recommending and producing based on UCC view history and production list, etc. of its producer. The recommender system is largely divided into filtering based on the preferred tag, UCC filtering used when producing the preferred UCC and creating process of recommended UCC using the Pearson formula. The recommender system in this thesis requires the data which were used when producing UCC. For that, we added the reference factor so that the data of UCC which were utilized when producing UCC into the existing metadata can be recorded. If the recommender system suggested in this thesis is used, the more effective and convenient UCC recommendation services with custom-made for producers can be provided.