• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.147-168
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• 2011

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

• Journal of Environmental Science International
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• v.26 no.5
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• pp.675-684
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• 2017

Provider-oriented weather information has been rapidly changing to become more customer-oriented and personalized. Given the increasing interest in wellness and health topics, the demand for health weather information, and biometeorology, also increased. However, research on changes in the human body according to weather conditions is still insufficient due to various constraints, and interdisciplinary research is also lacking. As part of an effort to change that, this study surveyed medical practitioners at an actual treatment site, using questionnaires, to investigate what kind of weather information they could utilize. Although there was a limit to the empirical awareness that medical staff had about weather information, most respondents noted that there is a correlation between disease and weather, with cardiovascular diseases (coronary artery disease (98.5%) and hypertension (95.9% ), skin diseases (atopic dermatitis (100%), sunburn (93.8%)) being the most common weather-sensitive ailments. Although there are subject-specific differences, most weather-sensitive diseases tend to be affected by temperature and humidity in general. Respiratory and skin diseases are affected by wind and solar radiation, respectively.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.758-769
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• 2020

Hybrid broadcasts utilizing heterogeneous networks can provide not only uniform broadcasting services but also various services using broadcast networks and communication networks. In particular, as content is consumed in various countries and regions, demands for personalized services continue to increase, and research on content insertion technology utilizing heterogeneous networks has been actively conducted. The most important technical challenge when inserting content based on heterogeneous networks is that the start of the inserted content, which replaces the original broadcast content at the time of content insertion, should proceed smoothly, and it must be able to accurately return to the original broadcast content. Currently, UHD broadcasting is converted to digital. However, since there is a system that supports the frame rate used in the analog method, when content insertion occurs in a conventional UHD broadcasting service, there is a problem in decoding the broadcast and inserted content. Since the replacement cost of the broadcasting system is astronomical, this paper proposes a content insertion method using by frame control that can support analog methods without replacing transmission equipment.

• Journal of Service Research and Studies
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• v.1 no.1
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• pp.29-48
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• 2011

Recently not only industry but also academy have shown an intense interest in social networking service. However, reckless imitation will not guarantee the successful eco-system of social networking service without rich understanding of growth driver and business model. Hence, this study aims at analyzing open platform strategy and business model conducted by a representative social networking service provider in order to provide platform operator, network operator, and portal provider with meaningful implications. Advertisers may pay great attention to social networking service because it has strong ability to provide users with spontaneous motivation to manage and update their profile, and these valuable information can be utilized for providing personalized advertisement on social networking service. As a result, one side of consumers in two side market, advertisers, tend to pay more expenditure to place advertisements. In addition, the open platform adopted by social networking service providers causes pro-sumers to participate in the eco-system, and thereby the explosive quantitative growth is realized. The fact of that this open social networking service can invade other web service area via an unified platform indicates that it may expand its service scope into a wide variety of web service areas. Hence, domestic portal services providers and network providers should consider social networking service not as one of new web services but as an disruptive service platform. Corresponding to the emergence of social networking service, especially if their business area is related to display advertising market, they should seek a way to provide social networking service access users's newly updated information and develop innovative media technologies to enter context awareness ads market.

• Journal of Digital Convergence
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• v.8 no.2
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• pp.89-102
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• 2010

The present thesis aims to analyze in consideration of recent changes in teaching environment in the universities the commercial digital contents and e-learning courses provided by the libraries, and to propose the ways to encourage university library service needed under the current situation. A survey of professors and students upon the quality of commercial digital contents service provided by the libraries in I University was made to measure its influence upon e-learning courses. The quality of commercial digital contents service provided by libraries was measured through Digital Library Service Quality Index (which will be referred as DL-SQL Model from here), which is used as a model to examine the Digital Library Service, with partial adjustments of 4 levels (information system service, digital books service, customer service quality, and customer community service) and 7 components (search possibility, an exclusive organization and interface, accessibility, digital books, customer support service, personalized service, and customer community). Among the library services in regard to digital contents, "customer service" and "customer community service" were analyzed to have stronger influence upon e-learning teaching and studying than quality-based service for "information system" and "digital books". Consequently, it is concluded that customized information service provided by the library for the professors who teach e-learning courses and their students is more influential to supporting e-learning courses than quantity pushing service through purchasing commercial digital contents, upon which the direction of digital contents policy to provide library services for e-learning courses should be based.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.665-667
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• 2014

Wellness is IT fused with the user manage and maintain the health of a service can help you. If you are using an existing Fitness Center to yourself by choosing appliances that fit with the risk of injury in order to learn how the efficient movement had existed for a long time was needed. To resolve, use the personal training but more expensive cost of people's problems, and shown again in the habit of exercising alone will have difficulty. This paper provides a variety of smart phones based on a hybrid app with compatibility with the platform and personalized training market system. Users of the Fitness Center is built into smart phones in the history of their movement sensors or transmits to the Web by typing directly. This is based on exercise programs tailored to users via the training market. Personal training marketplace has a variety of users, check the history of this movement he can recommend an exercise program for themselves can be applied by selecting the. This provides users with the right exercise program can do long-term exercise habits can be proactive and goal setting.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.155-178
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• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• 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.