• Journal of KIISE:Software and Applications
• /
• v.32 no.7
• /
• pp.581-591
• /
• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.33 no.4
• /
• pp.31-51
• /
• 2008

Predicting the preferences of users and providing the personalized services/products based on users' preferences is one of the important issues. However, the research considering users' preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose a framework for providing the personalized services based on context history in context-aware computing. Based on this framework, we have implemented a prototype system to show the feasibility of the framework. Previous researches have reasoned the preferences of the user considering only the user's input, but this research provides the personalized services using the relationship between users' profile and services.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.73-81
• /
• 2016

With the increasing popularity of social network services (SNSs), there have been many attempts to analyze the users of SNSs. By doing so, the characteristics and preferences of the users can be understood, which can help companies provide personalized information and services that they need or are relevant for them. This study aimed to analyze the usage behavior of Korean Twitter users from various perspectives to deepen the understanding of it. For this research goal, an online survey was conducted for the users of Twitter and the data about their actual usage were collected using the open API of Twitter. Factor analysis of the data revealed five factors that explain about 69.3% of the usage variables. It was also investigated how the factors are related to gender, age, and brand preferences. The results showed that the usage behavior of Twitter is largely affected by age (p<0.001), and also by gender through an interaction effect (p<0.05). Also, the factors showed significant statistical correlations with the brand preferences of the users.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.12
• /
• pp.2871-2877
• /
• 2015

Shopping patterns are changing with the emergence of SNS. Recently, it is also interested in providing the information based on the users' needs. Generally, the provided information is obtained from the history of users' simple browsing. Best selling hot item list is also provided in order to reflect the preferences of public users. However, the provided information is irrelevant to an individual preference. In this paper, we propose a method to utilize inner and outer SNS for analyzing public preferences about goods which are interested by individual users. The inner analyzing module collects and analyzes the preferences of community members about two goods designated by individual users. The outer analyzing module supports to analyze public preferences by using the tweeter SNS. The results of implementation show that it is possible to recommend goods based on the individual users' preferences unlike the existing shopping mall.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.5
• /
• pp.1260-1272
• /
• 2024

This paper tackles the prevalent challenges faced by existing tourism route recommendation methods, including data sparsity, cold start, and low accuracy. To address these issues, a novel intelligent tourism route recommendation method based on collaborative filtering is introduced. The proposed method incorporates a series of key steps. Firstly, it calculates the interest level of users by analyzing the item attribute rating values. By leveraging this information, the method can effectively capture the preferences and interests of users. Additionally, a user attribute rating matrix is constructed by extracting implicit user behavior preferences, providing a comprehensive understanding of user preferences. Recognizing that user interests can evolve over time, a weight function is introduced to account for the possibility of interest shifting during product use. This weight function enhances the accuracy of recommendations by adapting to the changing preferences of users, improving the overall quality of the suggested tourism routes. The results demonstrate the significant advantages of the approach. Specifically, the proposed method successfully alleviates the problem of data sparsity, enhances neighbor selection, and generates tourism route recommendations that exhibit higher accuracy compared to existing methods.

• International Journal of Computer Science & Network Security
• /
• v.21 no.11
• /
• pp.193-198
• /
• 2021

The application of e-Voting plays an important role in order to support democracy activities in Indonesia, such as elections at different levels. E-Voting has the function of providing better service to people in order to participate in elections. This research attempts to develop the appearance of the user interface of e-Voting based on users' emotional preferences using Kansei Engineering. Kansei Engineering is used in this research to analyze emotional feelings regarding e-Voting applications' presented Kansei words and give a recommendation on the most suitable user interface to be considered in their development. This research observed two main users' emotional feelings ("calm" and "formal") selected from ten Kansei words. The final recommendation is a conceptual element designed for designing e-Voting applications based on the Kansei word "calm".

• The Journal of the Korea Contents Association
• /
• v.16 no.4
• /
• pp.56-64
• /
• 2016

With the development of information technologies and the wide spread of smart devices, the number of users of social network services has increased exponentially. Studies that identify user preferences and recommend similar users in these social network services have been actively done. In this paper, we propose a new scheme to recommend social network friends with similar preferences through the moving pattern analysis of mobile users. The proposed scheme removes the meaningless trajectories via companions, short time trajectories, and repeated trajectories to determine the correct user preference. The proposed scheme calculates user similarity using the meaningful trajectories and recommends users with similar preferences as friends. It is shown through performance evaluation that the proposed scheme outperforms the existing schemes.

• Journal of Information Processing Systems
• /
• v.8 no.2
• /
• pp.191-212
• /
• 2012

Since a social network by definition is so diverse, the problem of estimating the preferences of its users is becoming increasingly essential for personalized applications, which range from service recommender systems to the targeted advertising of services. However, unlike traditional estimation problems where the underlying target distribution is stationary; estimating a user's interests typically involves non-stationary distributions. The consequent time varying nature of the distribution to be tracked imposes stringent constraints on the "unlearning" capabilities of the estimator used. Therefore, resorting to strong estimators that converge with a probability of 1 is inefficient since they rely on the assumption that the distribution of the user's preferences is stationary. In this vein, we propose to use a family of stochastic-learning based Weak estimators for learning and tracking a user's time varying interests. Experimental results demonstrate that our proposed paradigm outperforms some of the traditional legacy approaches that represent the state-of-the-art technology.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.3
• /
• pp.1065-1085
• /
• 2020

With rapid growth of content demands, device-to-device (D2D) content sharing is exploited to effectively improve the service quality of users. Considering the limited storage space and various content demands of users, caching schemes are significant. However, most of them ignore the influence of the asynchronous content reuse and the selfishness of users. In this work, the user preferences are defined by exploiting the user-oriented content popularity and the current caching situation, and further, we propose the social-aware rate, which comprehensively reflects the achievable contents download rate affected by the social ties, the caching indicators, and the user preferences. Guided by this, we model the collaborative caching problem by making a trade-off between the redundancy of caching contents and the cache hit ratio, with the goal of maximizing the sum of social-aware rate over the constraint of limited storage space. Due to its intractability, it is computationally reduced to the maximization of a monotone submodular function, subject to a matroid constraint. Subsequently, two social-aware collaborative caching algorithms are designed by leveraging the standard and continuous greedy algorithms respectively, which are proved to achieve different approximation ratios in unequal polynomial-time. We present the simulation results to illustrate the performance of our schemes.

• Journal of the Korean Home Economics Association
• /
• v.44 no.2 s.216
• /
• pp.71-81
• /
• 2006

The purpose of this study was to analyze the preferences for the physical features of senior congregate housing. The survey was conducted among middle-aged people in their fifties, who lived in Seoul, using the systematic random sampling method. The data were collected from November 3, 2003 to November 14, 2003 and the final subjects consisted of 498 respondents. Various statistical methods such as frequency, mean, cross tabulation, t-test, factor analysis, and multiple regression were used in this study. The results of this study were as follows. Firstly, most of the respondents preferred 55 to $70m^2$ sized individual units and they rarely wanted smaller units of less than $35m^2$. Individual units of one or two bedrooms were also preferred by future users. Small towns were preferred to large complex. For housing type, they preferred row houses or single detached houses to high-rise apartments. Secondly, there were no significant statistical differences between income and the preference of the physical features. From the results, we concluded that senior congregate housing should be developed not only in accordance with the users' preferences but also over a certain minimum physical quality level, regardless of the users' income.