• The Journal of the Korea Contents Association
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• v.15 no.12
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• pp.35-49
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• 2015

Recently, with the development of wireless communication technologies and mobile equipments, various studies on mobile P2P networks have been conducted. In this paper, we propose an advertisement dissemination method considering peer's mobilities and preferences in mobile P2P networks. The proposed scheme generates a peer's preference through the analysis of users' activities to disseminate advertisements with user preferences. To reduce duplicated advertising messages and improve reception ratio, the priority of advertisement re-dissemination is determined according to peer's preferences, mobilities, and advertisement reception ratio. We improve the participation rate of the advertisement dissemination of mobile peers using the incentive mechanism in mobile P2P networks. To show the superiority of the proposed scheme, we compare it with the existing scheme in terms of the number of messages and accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.10
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• pp.25-33
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• 2015

LED lightings combined with IT technology can provide variable illumination environments that can be controlled by users according to their emotional need and preferences. This is one of the most beneficial functions compared with conventional lightings offering only fixed color and brightness. There is however lack of analysis data for creating practical lighting solutions satisfying user preferences in a wide range of applications from residential to commercial places. To materialize the technical advantages of user-controllable LED lightings, more observation data are required in various situations. Therefore, dissimilar emotional needs are determined in the present study for compartmental office spaces (staff lounge room, meeting room and desk job place) through subjective experiments by 45 observers. The optimum lighting conditions (CCT and illuminance) are finally obtained using Response Surface Method and relevant prediction functions are also deduced. The final outcome can be applied for making user-preferred office illumination products.

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

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.549-558
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• 2007

Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.67-75
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• 2012

For the last few years, LBS has attracted considerable attention from many industries and societies as a result of propagated smart devices. LBS has a high utilization of mobile users as it uses user positions as a significant factor. Current LBS has only taken user position into account and it makes some limits. So, it is necessarily suggested that support for personalized services which consider user's motion, traffic condition, weather condition, time, personal information and preferences that have a huge impact on the accuracy. The purpose of this study is to design the inference systems with user's motion, preferences and schedules and provide users with the personalized information. To achieve this, Movement Ontology, User Profile Ontology, Schedule Ontology and Work Ontology should be constructed and based on this, smart applications were developed. Developed applications induced appropriately recommended results according to user's preference, motion and directions.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.311-315
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• 2009

The ubiquitous smart home is the home of future that takes advantage of context information from user and home environment and provides automatic home services for the user. User's location and motion are the most important contexts in the ubiquitous smart home. This paper presents a method positioning user's location using four cameras and some home context parameter together with user preferences provided. Some geometry math problems would be raised to figure out approximately which area is monitored by cameras. The moving object is detected within the image frames and then simulated in a 2D window to present visually where user is located and show his moving. The moving ways are statistically recorded and used for predicting user's future moving.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.174-190
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• 2010

In ubiquitous computing, invisible devices and software are connected to one another to provide convenient services to users [1][2]. Users hope to obtain a personalized service which is composed of customized devices among sharable devices in a ubiquitous smart space (which is called USS in this paper). However, the situations of each user are different and user preferences also are various. Although users request the same service in the same USS, the most suitable devices for composing the service are different for each user. For these user requirements, this paper proposes a device recommender system which infers and recommends customized devices for composing a user required service. The objective of this paper is the development of the systems for recommending devices through context-aware inference in peer-to-peer environments. For this goal, this paper considers the context and user preference. Also I implement a prototype system and test performance on the real ubiquitous mobile object (UMO).

• Journal of Distribution Science
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• v.13 no.8
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• pp.73-80
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• 2015

Purpose - This study's objective is to analyze the content of the communications between users and producers based on the construal level theory. User generated content refers to content created in an online-based service where users and producers communicate interactively with each other. In a user generated content platform, the messages sent and received between the many players, the users and producers who use the content, may be analyzed at the psychological level based on construal level theory. Research design, data, and methodology - This study gathered user and producer participation through a snow-bowling sampling method. The data analyzed includes 125 video clips and 2,912 comments. The period of the data collection was from September 2014 to December 2014. The collected data was analyzed using a t-test and two-way ANOVA. Results - This study obtained the following research results. First, users who were a short social distance from producers responded to user participatory activities stated in concrete language rather than abstract language. In contrast, users who were at a longer social distance from producers tended to respond to the content requesting user participation through abstract language. Second, if users and producers were at a short social distance from each other, user preference increased more when a producer response to user participation was expressed concretely rather than when it was expressed abstractly. In contrast, if the users were at a longer social distance, users' preferences increased more when producer response was expressed abstractly rather than when it was expressed concretely. Conclusion - This study found that the effect of suitability, in which the social distance and the content were in congruence at the construal level, could be observed. Therefore, based on this, academic and practical implications were drawn. The three main insights of the study are as follows. First, firms can use psychological factors to analyze the message content of users in their distribution platforms. This study reveals managerial implications for marketing managers who want to take make use of this analysis of user and producer communications. This study indicates that the main factors include the concrete and abstract scores and social distance between users and producers. Second, we also provide the strategic guidelines to maximizing user preferences and other outcomes. The main dependent variable in this study is the user preference shift; the variable increases through the congruence effect; and the construal level is determined by the social distance between the users and producers and the type of producer response. The outcomes here from users can be utilized to develop several systemic strategies. One process to use the outcomes could be: (1) firms could measure the users and producers social distance; (2) calculate the concreteness or abstractness of the messages; and, (3) predict the user preference outcomes by the congruence between user and producer social distance and the abstractness or concreteness of the message content.