• East Asian Journal of Business Economics (EAJBE)
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• v.2 no.4
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• pp.56-64
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• 2014

Today, travelers are provided large amount information which includes Web sites and tourist magazines about introduction of tourist spot. Many approaches have been proposed to analyze the large amount of available information with the aim of discovering the most popular Points of Tourist Interest and routes. However, it is not easy for users to process the information in a short time. Therefore travelers prefer to receive pertinent information easier and have that information presented in a clear and concise manner. Whether you are looking for banks by company, foreign exchange services, free wireless hotspots, touristic attractions, campsites, supermarkets, restaurants, cinemas, The aim of POI Tourism Services is to enable tourists to find spots that only the locals know, giving the tourists opportunity to the tourists to explore new areas of the place like never before. This paper proposes find the scope for a personalized service for tourist "Special Interest Tourism" recommendation for tourists who travel within India & for the benefit of Foreign Nationals who visit the country. The major focus of the study is to understand the demand for such a service being integrated into the conventional tour package. The major findings made during the course of the show that the market for "Special Interest Tourism" based services stands at approximately 63%. Travel today is mainly for the people from the middle income group having a fixed budget while traveling and would like economic travel solutions that fit their budget. This accounts for a major part of the market for the service. Most tourist prefer to go on week end getaways or trips that last more than a week, this means that a specialized trip plan based on the travelers interests is feasible with these type of travelers. Maximum demand for "Special Interest Tourism" based services would be during the festive seasons.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.39-45
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• 2018

Twitter users use 'Following', 'Retweet' and so on to find tweets that they are interested in. However, it is difficult for users to find tweets that are of interest to them on Twitter, which has more than 300 million users. In this paper, we developed a customized tweet recommendation system to resolve it. First, we gather current trends to collect tweets that are worth recommending to users and popular tweets that talk about trends. Later, to analyze users and recommend customized tweets, the users' tweets and the collected tweets are categorized. Finally, using Web service, we recommend tweets that match with user categorization and users whose interests match. Consequentially, we recommended 67.2% of proper tweet.

• Culinary science and hospitality research
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• v.15 no.3
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• pp.248-261
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• 2009

The purpose of this study is to examine the effects of selection attribute in big discount stores upon post-purchase consumer behavior and provide reference materials required for big discount stores to develop customer satisfaction strategies. As a result, this study shows the following findings: First, product-related factors had positive effects on post-purchase consumer behavior. Second, service-related factors had positive effects on post-purchase consumer behavior. Third, store-related factors had positive effects on post-purchase consumer behavior. Conclusively, it is advisable that big discount stores provide a variety of personalized services for customers to create and attract their trust, motivating effective recommendation to their acquaintances.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.15-21
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• 2016

Under the flag of creative economy, Korea government is now releasing public data in order to develop or provide a range of services. In this paper, we develop a customized trip navigation system to recommend a trip itinerary based on integration of open government data and personal tourist data. The system uses case-based reasoning (CBR) to provide a personalized trip navigation service. The main difference between existing trip information systems and ours is that our system can offers a user-oriented information service. In addition, our system supports Turn-key style contents provision to maximize convenience. Our system can be a good example of the way in which open government data can be used to design a new service.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.241-250
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• 2018

Online recruitment services have emerged as one of the most popular Internet services, providing job seekers with a comprehensive list of jobs and a search engine. But many recruitment services suffer from shortcomings due to their reliance on traditional client-pull information access model, in manay cases resulting in unfocused search results. Worldjob+, being operated by The Human Resources Development Service of Korea, addresses these problems and uses Splunk, a platform for analyzing machine data, to provide a more proactive and personalised services. It focuses on enhancing the existing system in two different ways: (a) using personalised automated matching techniques to proactively recommend most preferrable profile or specification information for each job opening announcement or recruiting company, (b) and to recommend most preferrable or desirable job opening announcement for each job-seeker. This approach is a feature-free recommendation technique that recommends information items to a given user based on what similar users have previously liked. A brief discussion about the potential benefit is also provided as a conclusion.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.2
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• pp.29-41
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• 2013

Purpose: Today, travelers are provided large amount information which includes Web sites and tourist magazines about introduction of tourist spot. Many approaches have been proposed to analyze the large amount of available information with the aim of discovering the most popular Points of Tourist Interest and routes. However, it is not easy for users to process the information in a short time. Therefore travelers prefer to receive pertinent information easier and have that information presented in a clear and concise manner. Research Design, Data and Methodology: Whether you are looking for banks by company, foreign exchange services, free wireless hotspots, touristic attractions, campsites, supermarkets, restaurants, cinemas, The aim of POI Tourism Services is to enable tourists to find spots that only the locals know, giving the tourists opportunity to the tourists to explore new areas of the place like never before. This paper proposes find the scope for a personalized service for tourist "Special Interest Tourism" recommendation for tourists who travel within India & for the benefit of Foreign Nationals who visit the country. Results: The major focus of the study is to understand the demand for such a service being integrated into the conventional tour package. The major findings made during the course of the show that the market for "Special Interest Tourism" based services stands at approximately 63%. Travel today is mainly for the people from the middle income group having a fixed budget while traveling and would like economic travel solutions that fit their budget. Conclusion: This accounts for a major part of the market for the service. Most tourist prefer to go on week end getaways or trips that last more than a week, this means that a specialized trip plan based on the travelers interests is feasible with these type of travelers. Maximum demand for "Special Interest Tourism" based services would be during the festive seasons.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.443-450
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• 2018

Due to the improvement of quality of life, health care is a main concern of modern people, and the demand for healthcare system is increasing naturally. However, it is difficult to provide customized wellness information suitable for a specific user because there are various medical information on the Internet and it is difficult to estimate the reliability of the information. In this study, we propose a user - centered service that can provide customized service suitable for users rather than simple search function by classifying big data as text mining and providing personalized medical information. We built a big data system and measured the data processing time while increasing the Hadoop slave node for efficient big data analysis. It is confirmed that it is efficient to build big data system than existing system.

• 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.