• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.1
• /
• pp.61-68
• /
• 2021

The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.10
• /
• pp.49-55
• /
• 2019

In this paper, we propose ontology-based recommendation system for supporting maintenance of Korean architectural heritage. This study includes the following: 1) design of ontology expressing repair information of architectural heritage, 2) creation of repair case DB, 3) creation of a recommendation system of repair method. For this study, we designed the ontology that expresses the information of Korean wooden building cultural heritage by referring to the existing heritage ontologies. Second, we created the repair information database based on the repair contents and the expert interview data provided by the National Institute of Cultural Heritage and the Cultural Heritage Administration. Third, we developed a system that recommends the repair method of Korean wooden architectural heritage with the most similar phenomena and causes. This study contributes to sharing repair knowledge and determining repair methods for architectural heritage repair.

• The Journal of the Korea Contents Association
• /
• v.9 no.5
• /
• pp.346-354
• /
• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• Journal of Internet Computing and Services
• /
• v.16 no.6
• /
• pp.133-141
• /
• 2015

Mass multimedia contents are shared through various social media servies including social network service. As social network reveals user's current situation and interest, highly satisfactory personalized recommendation can be made when such features are applied to the recommendation system. In addition, classifying the music by emotion and using analyzed information about user's recent emotion or current situation by analyzing user's social network, it will be useful upon recommending music to the user. In this paper, we propose a music recommendation method that makes an emotion model to classify the music, classifies the music according to the emotion model, and extracts user's current emotional state represented on the social network to recommend music, and evaluates the validity of our method through experiments.

• Journal of the Korean Society for information Management
• /
• v.39 no.3
• /
• pp.1-22
• /
• 2022

The purpose of this study is to propose a book recommendation system that can be used in school libraries. The book recommendation system applies an algorithm based on association rules using DLS lending data and is designed to provide personalized book recommendation services to school library users. For this purpose, association rules based on the Apriori algorithm and betweenness centrality analysis were applied and detailed functions such as descriptive statistics, generation of association rules, student-centered recommendation, and book-centered recommendation were materialized. Subsequently, opinions on the use of the book recommendation system were investigated through in-depth interviews with teacher librarians. As a result of the investigation, opinions on the necessity and difficulty of book recommendation, student responses, differences from existing recommendation methods, utilization methods, and improvements were confirmed and based on this, the following discussions were proposed. First, it is necessary to provide long-term lending data to understand the characteristics of each school. Second, it is necessary to discuss the data integration plan by region or school characteristics. Third, It is necessary to establish a book recommendation system provided by the Comprehensive Support System for Reading Education. Based on the contents proposed in this study, it is expected that various discussions will be made on the application of a personalization recommendation system that can be used in the school library in the future.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.731-737
• /
• 2018

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

• Journal of Digital Contents Society
• /
• v.19 no.4
• /
• pp.701-709
• /
• 2018

Our research team implemented and operated the system by analyzing the membership information and identifying the different preferences for each group and providing the results of the recommendation based on accumulated membership information and activity log data to the individual. The utilization log was followed up. We analyzed how many people use recommended services and analyzed whether there are any factors other than the personalization service algorithm that affect the service utilization of the system with personalization. In addition, we propose recommendation methods based on behavioral changes when incentives are given through analyzing patterns of users' usage according to methods of recommending services and contents that are often used based on analysis contents.

• Journal of Korea Society of Industrial Information Systems
• /
• v.18 no.4
• /
• pp.73-79
• /
• 2013

Recently, smart TVs have widely spread in our daily lives. However, it is difficult for users to find proper TV contents among a lot of TV and application contents because of inconvenience of input devices compared with those of smart phones or smart pads, so there are some problems with very low utilization of the smart functionalities of smart TVs. We suggest a personalized contents recommender system on smart TVs to resolve these problems and help for users to search appropriate contents easily and quickly in this research. We design and implement an intelligent system for personalized contents recommendation on the smart TVs based on multi-dimensional analysis considering user profiles and preferences, watching patterns of TV programs, and TV contents use statistics of TV users.

• Journal of Internet Computing and Services
• /
• v.17 no.5
• /
• pp.75-87
• /
• 2016

With the exploration of digital academic information, it is certainly required to develop more effective academic contents recommender system in order to accommodate increasing needs for accessing more personalized academic contents. Considering historical usage data, the academic content recommender system recommends personalized academic contents which corresponds with each user's preference. So, the academic content recommender system effectively increases not only the accessibility but also usability of digital academic contents. In this paper, we propose the new journal recommendation technique based on information of journal subscription and web usage logs in order to properly recommend more personalized academic contents. Our proposed recommendation method predicts user's preference with the institution similarity, the journal similarity and journal importance based on citation relationship data of references and finally compose institute-oriented recommendations. Also, we develop a recommender system prototype. Our developed recommender system efficiently collects usage logs from distributed web sites and processes collected data which are proper to be used in proposed recommender technique. We conduct compare performance analysis between existing recommender techniques. Through the performance analysis, we know that our proposed technique is superior to existing recommender methods.

• The Journal of the Korea Contents Association
• /
• v.11 no.2
• /
• pp.22-30
• /
• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.