• Journal of the Korea Society of Computer and Information
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• v.19 no.5
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• pp.61-69
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• 2014

Collaborative filtering recommendation creates similar item subset or similar user subset based on user preference about items and predict user preference to particular item by using them. Thus, if preference matrix has low density, reliability of recommendation will be sharply decreased. To solve these problems we suggest Hybrid Preference Prediction Technique Using Weighting based Data Reliability. Preference prediction is carried out by creating similar item subset and similar user subset and predicting user preference by each subset and merging each predictive value by weighting point applying model condition. According to this technique, we can increase accuracy of user preference prediction and implement recommendation system which can provide highly reliable recommendation when density of preference matrix is low. Efficiency of this system is verified by Mean Absolute Error. Proposed technique shows average 21.7% improvement than Hao Ji's technique when preference matrix sparsity is more than 84% through experiment.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.111-121
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• 2009

In this paper, we propose a multimedia recommender system using user's playback time. Proposed system collects multimedia content which is requested by user and its user‘s playback time, as web log data. The system predicts playback time.based preference level and related contents from collected transaction database by fuzzy association rule mining. Proposed method has a merit which sorts recommendation list according to preference without user’s custom preference data, and prevents a false preference. As an experimental result, we confirm that proposed system discovers useful rules and applies them to recommender system from a transaction which doesn‘t include custom preferences.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.651-658
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• 2006

Mobile computing environment is known to be quite difficult to provide user with a stable QoS(Qualify of Service) due to its mobility nature. In order to protect the inherent characteristics of wireless network such as low bandwidth and high transmission delay along with the user's mobility, many works are conducted to apply caching and prefetching methods. This paper presents a novel prefetching technique which is based on user's preference, that is, interest and Uuかity. It tries to improves the effectiveness of prefetching by separating and appling the interest with personal tendency of a given information, and the popularity with general tendency of the information. The proposed scheme shown relatively superior performance in terms of the utilization ratio of prefetched information and the failure ratio of information retrieval than the existing methods.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.271-277
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• 2013

Typical semantic search query for Semantic search promises to provide more accurate result than present-day keyword matching-based search by using the knowledge base represented logically. Existing keyword-based retrieval system is Preference for the semantic interpretation of a user's query is not the meaning of the user keywords of interconnect, you can not search. In this paper, we propose a method that can provide accurate results to meet the user's search intent to user preference based evaluation by ranking search. The proposed scheme is Integrated ontology-based knowledge base built on the formal structure of the semantic interpretation process based on ontology knowledge base system.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.799-805
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• 2010

In this paper, we propose an intelligent navigation system that selects a proper route for user and applies the user's preference, user's tendency and environmental state estimated by driving information of user and road state. The system uses data of sensors, navigation and intelligent transport system to evaluate conditions of roads and it considers state of user's emotion. The system also uses soft-computing method to infer and learn the user's preference and tendency. We verify the proposed algorithm by computer simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.689-691
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• 2011

Due to the rapid development of Information and communication, the technology of multimedia presentation technology is evolving into the service that user can actively, realistically enjoy and play based on user's preference and taste not only for User's passive service. Especially, the industry related the realistic multimedia service that supports targeting Human emotion with the property of Human hearing is expected to be formed of the high value-added premium market. Audio technology is affected on human's emotion and the viewing environment around than video technology. Also the audio technology compared to video technology is a research part that appeals to human emotion and emphasize on psychological aspects. With this viewpoint, the development of intelligent and realistic audio technology needs highly specialty. In this study, "intelligent real-sound presentation technology" that support high quality and realistic audio and the "core technologies" that are composing of this will be introduced.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.2
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• pp.74-80
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• 2013

In this study, a novel and flexible recommender system was developed, based on product taxonomy and usage patterns of users. The proposed system consists of the following four steps : (i) estimation of the product-preference matrix, (ii) construction of the product-preference matrix, (iii) estimation of the popularity and similarity levels for sought-after products, and (iv) recommendation of a products for the user. The product-preference matrix for each user is estimated through a linear combination of clicks, basket placements, and purchase statuses. Then the preference matrix of a particular genre is constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product with respect to the total. The popularity and similarity levels of a user's clicked product are estimated with an entropy index. Based on this information, collaborative and content-based filtering is used to recommend a product to the user. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site. Our results clearly showed that the proposed hybrid method is superior to conventional methods.

• Industrial Engineering and Management Systems
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• v.10 no.4
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• pp.238-246
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• 2011

Given a directed graph, we can determine how the user's preference moves from one product item to another. In this graph called "preference transition network", each node represents the product item while its edge pointing to the other nodes represents the transition of user's preference. However, with the large number of items make the network become more complex, unclear and difficult to be interpreted. In order to address this problem, this paper proposes a visualization technique in preference transition analysis based on recency and frequency. By adapting these two elements, the semantic meaning of each item and its transition can be clearly identified by its different types of node size, color and edge style. The experiment in a sales data has shown the results of the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5234-5251
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• 2018

It is well-known that virtualization technology can bring many benefits not only to users but also to service providers. From the view of system security and resource utility, higher resource sharing degree and higher system reliability can be obtained by the introduction of virtualization technology in distributed cloud. The small size time-sharing multiplexing technology which is based on virtual machine in distributed cloud platform can enhance the resource utilization effectively by server consolidation. In this paper, the concept of memory block and user satisfaction is redefined combined with user requirements. According to the unbalanced memory resource states and user preference requirements in multi-virtual machine environments, a model of proper memory resource allocation is proposed combined with memory block and user satisfaction, and at the same time a memory optimization allocation algorithm is proposed which is based on virtual memory block, makespan and user satisfaction under the premise of an orderly physical nodes states also. In the algorithm, a memory optimal problem can be transformed into a resource workload balance problem. All the virtual machine tasks are simulated in Cloudsim platform. And the experimental results show that the problem of virtual machine memory resource allocation can be solved flexibly and efficiently.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.