• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.2
• /
• pp.277-284
• /
• 2017

In a flood of information, most users will want to get a proper recommendation. If a recommender system fails to give appropriate contents, then quality of experience (QoE) will be drastically decreased. In this paper, we propose a recommender system based on the intra-cluster users' item preference for improving recommendation accuracy indices such as precision, recall, and F1 score. To this end, first, users are divided into several clusters based on the actual rating data and Pearson correlation coefficient (PCC). Afterwards, we give each item an advantage/disadvantage according to the preference tendency by users within the same cluster. Specifically, an item will be received an advantage/disadvantage when the item which has been averagely rated by other users within the same cluster is above/below a predefined threshold. The proposed algorithm shows a statistically significant performance improvement over the item-based collaborative filtering algorithm with no clustering in terms of recommendation accuracy indices such as precision, recall, and F1 score.

• The KIPS Transactions:PartC
• /
• v.13C no.4 s.107
• /
• pp.415-426
• /
• 2006

The essential characteristic of ubiquitous is context-awareness, and that means ubiquitous computing can automatically process the data that change according to space and time, without users' intervention. However, in circumstance of context awareness, since location information is able to be collected without users' clear approval, users cannot control their location information completely. These problems can cause privacy issue when users access their location information. Therefore, it is important to construct the location information system, which decides to release the information considering privacy under the condition such as location, users' situation, and people who demand information. Therefore, in order to intercept an outflow information and provide securely location-based information, this paper suggests a new system based CS-RBAC with the existing LBS, which responds sensitively as customer's situation. Moreover, it accommodates a merit of PCP reflecting user's preference constructively. Also, through privacy weight, it makes information not only decide to providing information, but endow 'grade'. By this method, users' data can be protected safely with foundation of 'Role' in context-aware circumstance.

• Journal of Digital Convergence
• /
• v.15 no.4
• /
• pp.461-468
• /
• 2017

The current available recommendation systems for mobile games have a couple of problems. First, there is no knowing whether they make a pattern recommendation for games that actual users prefer or for games that they are simply interested in. It is also impossible to know the subjective preference of users in a direct manner. An AHP(Analytic Hierarchy Process)-based recommendation system for mobile games was thus developed to reflect the subjective preference of users directly, but it had its own problem since the degree of preference could vary among users in spite of the same scale for their preferable items. In an effort to solve those problems, this study implemented a recommendation system for mobile games by applying triangular fuzzy numbers of the Fuzzy-AHP technique and the independence of evaluation items in the Bayesian Network. The findings show that the proposed recommendation system recorded the highest accuracy of recommendation results and the highest level of user satisfaction.

• Database Research
• /
• v.34 no.3
• /
• pp.86-98
• /
• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

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

• Korean Institute of Interior Design Journal
• /
• v.16 no.1 s.60
• /
• pp.56-64
• /
• 2007

Community facilities in apartment complex have been developed through residents' needs for housing environmental amenities and social trend for 'Well-being' combined with marketing competition among construction companies. But community facilities and fitness centers which are in the initial stage of development are not well fit with residents' needs because the designers plan the community facilities without considering on residents' life-style and preference. This study investigates the present status of fitness center, and surveys the residents' preference for the proposed fitness center design. The result includes that fitness center users in apartment complex want a convenient and comfortable resting spaces similar level with those in fitness center of mixed-use residential building. A resting space provides opportunity that community members meet each other as well as they can rest after exercise. The result also shows that the fitness center users prefer wood floor and wall as interior finishing materials in exercising space and resting space, which users think, gives more comfortable and splendid feeling.

• Journal of Information Technology Services
• /
• v.20 no.3
• /
• pp.57-73
• /
• 2021

Recently, online job websites have been activated as unemployment problems have emerged as social problems and demand for job openings has increased. However, while the online job platform market is growing, users have difficulty choosing their jobs. When users apply for a job on online job websites, they check various information such as job contents and recruitment conditions to understand the details of the job. When users choose a job, they focus on various details related to the job rather than simply viewing and supporting the job title. However, existing online job websites usually recommend jobs using only quantitative preference information such as ratings. However, if recommendation services are provided using only quantitative information, the recommendation performance is constantly deteriorating. Therefore, job recommendation services should provide personalized services using various information about the job. This study proposes a recommended methodology that improves recommendation performance by elaborating on qualitative preference information, such as details about the job. To this end, this study performs a topic modeling analysis on the job content of the user profile. Also, we apply LDA techniques to explore topics from job content and extract qualitative preferences. Experiments show that the proposed recommendation methodology has better recommendation performance compared to the traditional recommendation methodology.

• Journal of applied mathematics & informatics
• /
• v.28 no.1_2
• /
• pp.439-450
• /
• 2010

In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

• Journal of information and communication convergence engineering
• /
• v.18 no.1
• /
• pp.55-60
• /
• 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)
• /
• v.4 no.2
• /
• pp.174-190
• /
• 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).