• Journal of the Korean Data and Information Science Society
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• 제18권3호
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• pp.659-668
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• 2007

Collaborative filtering approach for recommender systems are now widely applied in e-commerce to assist customers to find their needs from many that are frequently available. this approach makes recommendations for users based on the opinions to similar users in the system. But this approach is opened to users who present their preference to items or acquire the preference information form other users, noise in the system makes significant problem for accurate recommendation. In this paper, we analyze the relationship between the standard deviation of preference ratings for each user and the estimated ratings of them. The result shows that the possibility of the pre-filtering condition which detecting the factor of bad effect on the prediction of user's preference. It is expected that using this result will reduce the possibility of bad effect on recommender systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• 대한인간공학회지
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• 제36권3호
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• pp.197-211
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• 2017

Objective: We propose to apply color information to facilitate finding books in the library. Background: Currently, books are classified in the basis of a decimal classification system and a call number in the library. Users find a book using the call number. However, this classification system causes various difficulties. Method: In a process analysis and survey study, we identify what the real problem is and where the problem is occurred. To solve the real problems, we derived a new search method using color information. We conducted a comparative experiment with 48 participants to see whether the new method can show higher performance. Results: The new method using color information showed faster time and higher subjective rating scores than current call number method. Also, the new method showed faster time regardless of the skill level while the call number method showed time differences in terms of the skill level. Conclusion: The effectiveness of the proposed method was verified by experiments. Users will be able to find the desired book without difficulty. This method can improve the quality of service and satisfaction of library use. Application: Our book search method can be applied as a book search tool in a real public library. We hope that the method can provide higher satisfaction to users.

• Journal of Computing Science and Engineering
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• 제7권1호
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• pp.21-29
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• 2013

The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create fine-grained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users' ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3319-3338
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• 2016

Cloud storage is becoming more and more popular because of its elasticity and pay-as-you-go storage service manner. In some cloud storage scenarios, the data that are stored in the cloud may be shared by a group of users. To verify the integrity of cloud data in this kind of applications, many auditing schemes for shared cloud data have been proposed. However, all of these schemes do not consider the access authorization problem for users, which makes the revoked users still able to access the shared cloud data belonging to the group. In order to deal with this problem, we propose a novel public auditing scheme for shared cloud data in this paper. Different from previous work, in our scheme, the user in a group cannot any longer access the shared cloud data belonging to this group once this user is revoked. In addition, we propose a new random masking technique to make our scheme preserve both data privacy and identity privacy. Furthermore, our scheme supports to enroll a new user in a group and revoke an old user from a group. We analyze the security of the proposed scheme and justify its performance by concrete implementations.

• Journal of Information Processing Systems
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• 제5권3호
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• pp.151-158
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• 2009

This paper proposes a utility-based data rate allocation algorithm to provide high-quality mobile video streaming over femtocell networks. We first derive a utility function to calculate the optimal data rates for maximizing the aggregate utilities of all mobile users in the femtocell. The total sum of optimal data rates is limited by the link capacity of the backhaul connections. Furthermore, electromagnetic cross-talk poses a serious problem for the backhaul connections, and its influence passes on to mobile users, as well as causing data rate degradation in the femtocell networks. We also have studied a fixed margin iterative water-filling algorithm to achieve the target data rate of each backhaul connection as a counter-measure to the cross-talk problem. The results of our simulation show that the algorithm is capable of minimizing the transmission power of backhaul connections while guaranteeing a high overall quality of service for all users of the same binder. In particular, it can provide the target data rate required to maximize user satisfaction with the mobile video streaming service over the femtocell networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• 한국멀티미디어학회논문지
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• 제25권5호
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• pp.703-720
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• 2022

Based on the love styles theory used in psychology, this paper classifies users(Passionate Love, Game-playing Love, Friendship Love, Practical Love, Possessive Love, Altruistic Love) and investigates satisfaction with the motivation for using TikTok love videos(Entertainment, Social Relationship, Love skills-learning, Self-verification, Problem-solving) according to the theory of use and satisfaction. First, 414 users were selected to conduct TikTok surveys to collect data. Then, through the analysis of the research results, among the six love types, game-playing type and possessive type have a positive (+) impact on entertainment motivation and love skill-learning motivation. Game-playing type also have a positive (+) impact on social relationship motivation and self-verification motivation. In addition, altruistic type and possessive type are also factors to strengthen the motivation of self-verification. The altruistic type, possessive type and practical type will improve the problem-solving motivation. Finally, through hierarchial multiple regression analysis, it is confirmed that game-playing love type, entertainment motivation, love skill-learning motivation and self-verification motivation can improve user satisfaction. The above results enrich the research of user classification as well as providing inspiration for improving the quality and communication efficiency of TikTok's video and enhancing user experience.

• Journal of Mechanical Science and Technology
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• 제14권9호
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• pp.956-967
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• 2000

In this paper, we explore the issues of force display in the cooperative virtual environment shared by multiple users distributed over the network with heterogeneous hardware platforms. The proposed method is to cope with the problem of small time delay and the difference of sampling rate in the distributed configuration. In the proposed approach the interaction forces of the participants are just treated as the independent sources of acceleration. Thus the action of a participant simply changes the acceleration of the virtual object and consequently the states of the virtual object will be updated. When the updated states are reported to all the participants, the information on the time of state changes is delivered, too. Employing the discrete state information updated by the other users, each user modifies his own virtual environment and pseudo-realtime simulation can be realized. Excluding the software interface and the communication technique, it is proposed the simulation method for the operation of respective users and the way of calculating the driving input to the display device. For experimental verification we construct a cooperative virtual environment shared by two remote users and outline the results of experiments.

• 한국콘텐츠학회논문지
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• 제10권10호
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• pp.9-18
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• 2010

디지털 콘텐츠의 양이 방대해지면서 사용자가 원하는 디지털 콘텐츠를 검색하는 데 많은 시간이 필요하다. 그러므로 방대한 디지털 콘텐츠로부터 사용자가 원하는 콘텐츠를 제공하기 위해서는 디지털 콘텐츠를 분석하여 사용자에게 적합한 콘텐츠를 추출하는 기술이 필요하다. 그리고 빠른 시간 내에 사용자에게 적합한 디지털 콘텐츠를 찾기 위해서는 디지털 콘텐츠에 대한 필터링 기술이 필요하다. 본 논문에서는 개인에게 적합한 디지털 콘텐츠를 필터링하는 기법을 제안한다. 본 논문에서 제안한 기법은 디지털 콘텐츠에 대한 사례기반 정보를 분석하여 개인 사용자에게 적합한 디지털 콘텐츠를 제공한다. 사용자의 선호도 분석에는 디지털 콘텐츠 사용에 대한 사례를 이용한다. 다양한 시뮬레이션을 통해 제안한 기법의 효과를 확인하였다.