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Enhancing the Performance of Recommender Systems Using Online Review Clusters
Giseop Noh, Hayoung Oh, Jaehoon Lee
http://doi.org/10.5626/JOK.2018.45.2.126
The recommender system (RS) has emerged as a solution to overcome the constraints of excessive information provision and to maximize profit and reputation for information providers. Although the RS can be implemented with various approaches, there is no study on how to appropriately utilize the information generated from the review of the recommended object. We propose a method to improve the performance of RS by using cluster information generated from online review. We implemented the proposed method and experimented with real data, and confirmed that the performance is significantly improved compared to the existing approaches.
Message Delivery Techniques using Group Intimacy Information among Nodes in Opportunistic Networks
Seohyang Kim, Hayoung Oh, Chongkwon Kim
In opportunistic networks, each message is delivered to the destination by repeating, storing, carrying, and forwarding the message. Recently, with the vitalization of social networks, a large number of existing articles have shown performance improvement when delivering the message and considering its social relational networks. However, these works only deliver messages when they find nodes, assuming that every node cooperates with each other unconditionally. Moreover, they only consider the number of short-term contacts and local social relations, but have not considered each node’s average relation with the destination node. In this paper, we propose novel message sending techniques for opportunistic networks using nodes’ social network characteristics. In this scheme, each message is delivered to the destination node with fewer copies by delivering it mostly through nodes that have high intimacy with the destination node. We are showing that our proposed scheme presents a 20% performance increase compared to existing schemes.
A Re-configuration Scheme for Social Network Based Large-scale SMS Spam
Sihyun Jeong, Giseop Noh, Hayoung Oh, Chong-Kwon Kim
The Short Message Service (SMS) is one of the most popular communication tools in the world. As the cost of SMS decreases, SMS spam has been growing largely. Even though there are many existing studies on SMS spam detection, researchers commonly have limitation collecting users" private SMS contents. They need to gather the information related to social network as well as personal SMS due to the intelligent spammers being aware of the social networks. Therefore, this paper proposes the Social network Building Scheme for SMS spam detection (SBSS) algorithm that builds synthetic social network dataset realistically, without the collection of private information. Also, we analyze and categorize the attack types of SMS spam to build more complete and realistic social network dataset including SMS spam.
A Network Coding Based Green Cognitive Radio Network
With the rapid increase of energy consumption and environmental problems, the need for green techniques is increasing. Network coding can provide a solution by reducing unnecessary data transmission and by estimating traffic patterns. In addition, it can amplify the synergy with the cognitive radio network (CR) since the CR has recognition and optimal decision functionalities. In this paper, we propose a network coding based green cognitive radio network. With the simulations, we show that the proposed scheme is up to 25% better than the previous work.
Designing an Algorithm for the Priority Deciding and Recommending of the Logistic Support with Stationary Distribution
Giseop Noh, Sihyun Jeong, Chong-Kwon Kim, Hayoung Oh
One of the important roles used to ensure victory in a war is to maximize the overall military forces and to make sure that the capability of the military forces can be sustained as much as possible. Although several researchers have proposed various possible methodologies for logistics support, no research trials have been undertaken to investigate logistics support that considers all relevant elements of such. Unlike previous in trials that consider and analyze the system fault ratio as the main methodology, we propose an approach that simultaneously decides and recommends logistic priority by reflecting and combining item costs, transportation, fault-ratio, and system complexity. Also, we designed an algorithm that can recommend optimized logistics support priority using stationary distribution.
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