• Journal of KIISE:Software and Applications
• /
• v.33 no.3
• /
• pp.356-364
• /
• 2006

'Ubiquitous Computing' is the most important paradigm of the next generation Information-Communication technology. The one of important problems to develop ubiquitous computing service system get hold of relations between times of transfer objects and events of transfer objects. Another problem is what reason transfer-pattern through location data of transfer objects. In this paper, we propose an approach to offer temporal-relation service in ubiquitous computing environment. The first is temporal reasoning in service viewpoint. The second is temporal reasoning to record user's preference. Users have preferences that are closely connected with time. These preferences are recorded at user profile. Therefore, the user profile-based ubiquitous service system can offer suitable service to users.

• The KIPS Transactions:PartA
• /
• v.11A no.7 s.91
• /
• pp.529-536
• /
• 2004

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a user's preference. The context-aware middle-ware utilizes 6 basic data for learning and predicting the user's preference on the multimedia content : the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The log manager module maintains history information for multimedia content chosen by the user. The user-pattern learning and pre-dicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an in-dividual's preferences can be effectively evaluated and predicted by adopting the proposed context model.

• Fashion & Textile Research Journal
• /
• v.25 no.5
• /
• pp.578-592
• /
• 2023

This study identified and evaluated by deriving and categorizing concepts related to the user experience of metaverse fashion shows using grounded theory, which is a qualitative research method. Based on experiential marketing theory, in-depth interviews were conducted for 14 days with 14 males and females in their 20s and 30s. The research results and contents are as follows: The causal condition was the purpose of using metaverse fashion shows, and the action/interaction strategy caused by such a case was found to be establishing a system for metaverse fashion shows and promoting a positive brand image. The results included content evaluation of satisfaction, normal, or dissatisfaction. The contextual condition was a change in the form of consumption that emphasized experience, while the interventional condition was psychological distance. Based on this, the core category was defined as "consumption patterns that emphasized the purpose of use and experience affects the metaverse fashion shows and psychological distance appeared as a user experience evaluation through the establishment of a system of metaverse fashion shows and the promotion of a positive brand image". User types were classified as active or passive. Active users have the autonomy to select content according to their individual preferences, and accordingly, their experience preference tends to change. In contrast, passive users' preference for the technical quality of content is relatively low, but they have a high concentration of content diversity and audio-visual interest elements.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.43-56
• /
• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Journal of the Korean Society of Food Culture
• /
• v.24 no.6
• /
• pp.649-654
• /
• 2009

This study has a purpose of suggesting the basic data to achieve customer satisfaction by understanding the preference of each type of restaurant industry for the taste of customers in 20's referring to 8 images. In the preference for style of image in dining space, the participants responded that they prefer natural, modern and romantic image, and both male and female participants preferred natural image. Participants responded that they prefer natural, romantic and modern in sequence as their general preference for style of image in dining space, and male participants preferred modern and natural but female participants preferred romantic and natural. The survey that was conducted for different menus has suggested that the reasonable image for fast food is casual, hard casual and classic for hotel restaurant, casual for school restaurant, romantic for cafe, casual for western restaurant, simple for Japanese restaurant, classic and elegance for Chinese restaurant and natural for Korean restaurant. According to the result of the analysis of dining space image, factor 1 are called 'cold image (CI)' as they have simple and modern image, factor 2 are called 'soft image (SI)' as they have natural and romantic image, factor 3 are called 'warm image (WI)' as they have casual and elegance image and factor 4 are called 'hard image (HI)' as they have classic image.

• The Journal of the Korea Contents Association
• /
• v.16 no.11
• /
• pp.91-102
• /
• 2016

This paper investigates the relationship between the Pay TV subscriber's genre preference and VOD purchase by analyzing actual purchase data of movie VOD of IPTV subscribers for 8 months. The result shows as follows. First, in case of purchasing movie contents below 4000 won, user's genre preference was higher than that of using contents over 4,000 won. This means the subscribers tend to follow their genre preference when the mass-typed recommendation is limited. Second, those who purchase foreign contents show higher genre preference than those who purchase domestic movies. Third, subscribers who purchase more frequently and much more tend to use more diverse genres. Heavy users or those who have higher willingness to pay would consume more diverse contents. It implies that VOD use would increase by supplying the personal recommendation service based on the subscriber's genre preference.

• Journal of Distribution Science
• /
• v.12 no.9
• /
• pp.53-63
• /
• 2014

Purpose - The rapid spread of SNS and increase of SNS users have heralded great changes in the tourism industry. Therefore, this study focused on how SNS characteristics- usefulness, convenience, interactivity, and intimacy - influence diffusivity, reliability and, consequently, user's preference for tourist attractions. Research design, data, and methodology - This study is designed not only to collect data with a questionnaire survey but also to test hypotheses with SEM by SPSS 18.0 and AMOS 18.0. Results - Usefulness, interactivity, and intimacy positively affect diffusivity, whereas convenience does not positively affect diffusivity. In addition, intimacy has a negative influence on reliability. However, diffusivity and reliability have positive impacts on the preference for tourist places. Conclusions - Certain characteristics of SNS facilitates the spreading of SNS tourist information. Usability and interactivity have positive impacts on the reliance of tourist information. Better communication can enhance the reliance of travel information. The influence of spreading tourist information has a positive influence on its reliance. Extension and reliance can have positive effects on the preference for tourist attractions.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.1-18
• /
• 2016

Collaborative Filtering (CF) predicts the focal user's preference for particular item based on user's preference rating data and recommends items for the similar users by using them. It is a popular technique for the personalization in e-commerce to reduce information overload. However, it has some limitations including sparsity and scalability problems. In this paper, we use a method to integrate social network information into collaborative filtering in order to mitigate the sparsity and scalability problems which are major limitations of typical collaborative filtering and reflect the user's qualitative and emotional information in recommendation process. In this paper, we use a novel recommendation algorithm which is integrated with collaborative filtering by using Social SVD++ algorithm which considers social network information in SVD++, an extension algorithm that can reflect implicit information in singular value decomposition (SVD). In particular, this study will evaluate the performance of the model by reflecting the real-world user's social network information in the recommendation process.

• Korean Institute of Interior Design Journal
• /
• v.19 no.4
• /
• pp.66-73
• /
• 2010

The purpose of this study is to develop the basis data for methods of the plan to make the office space of the psychological environment by survey of preference to users in Department Store. The results are as follows : First, we could know that the space plan has to be considered the privacy and sociality for the psychological environment in office space. Second, we could know that there are efficient methods of space plan to use the cutoff division by partitions, the psychological division by furniture and the sensitive division by material for privacy in office and resting space. For sociality, there are efficient arrangement methods to plan by division type in office space and by opening type in resting space. And it is good method to use by color that is one in esthetical elements. Third, if we considered users, it is good method to use partitions to cutoff from the floor to the ceiling for privacy in office space and to use movable furnitures to cutoff for privacy in resting space. Forth, we could know that there is the difference in methods of space plan to be preferred by user's variables for privacy and sociality in office and resting spaces. Thus, i think that we have to study considering user's variables and types of department store.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.3
• /
• pp.819-830
• /
• 2008

Web recommender system was suggested in order to solve the problem which is cause by overflow of information. Collaborative filtering is the technique which predicts and recommends the suitable goods to the user with collection of preference information based on the history which user was interested in. However, there is a difficulty of recommendation by lack of information of goods which have less popularity. In this paper, it has been researched the way to select the sparsity of goods and the preference in order to solve the problem of recommender system's sparsity which is occurred by lack of information, as well as it has been described the solution which develops the quality of recommender system by selection of customers who were interested in.