• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.6
• /
• pp.684-695
• /
• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.5
• /
• pp.1569-1586
• /
• 2015

This paper considers the optimal cell association and resource allocation for load balancing in a heterogeneous cellular network subject to user's quality-of-service (QoS) constraints. We adopt the proportional fairness (PF) utility maximization formulation which also accommodates the QoS constraints in terms of minimum rate requirements. With equal resource allocation this joint optimization problem is either infeasible or requires relaxation that yields a solution which is difficult to implement. Nevertheless, we show that this joint optimization problem can be effectively solved without any priori assumption on resource allocation and yields a cell association scheme which enforces single BS association for each user. We re-formulated the joint optimization problem as a network-wide resource allocation problem with cardinality constraints. A reweighted heuristic l₁-norm regularization method is used to obtain a sparse solution to the re-formulated problem. The cell association scheme is then derived from the sparsity pattern of the solution, which guarantees a single BS association for each user. Compared with the previously proposed method based on equal resource allocation, the proposed framework results in a feasible cell association scheme and yields a robust solution on resource allocation that satisfies the QoS constraints. Our simulations illustrate the impact of user's minimum rate requirements on cell association and demonstrate that the proposed approach achieves load balancing and enforces single BS association for users.

• The Journal of the Korea Contents Association
• /
• v.7 no.11
• /
• pp.23-33
• /
• 2007

The collaborative filtering has used the nearest neighborhood method based on the preference and the similarity using the Pearson correlation coefficient. Therefore, it does not reflect content of the items and has the problems of the sparsity and scalability as well. the item-based collaborative filtering has been practically used to improve these defects, but it still does not reflect attributes of the item. In this paper, we propose the user and item based collaborative filtering using the classification property and Naive Bayesian to supplement the defects in the existing recommendation system. The proposed method complexity refers to the item similarity based on explicit data and the user similarity based on implicit data for handing the sparse problem. It applies to the Naive Bayesian to the result of reference. Also, it can enhance the accuracy as computation of the item similarity reflects on the correlative rank among the classification property to reflect attributes.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.7C
• /
• pp.554-564
• /
• 2012

The vehicle recognition consists of two steps; the vehicle region detection step and the vehicle identification step based on the feature extracted from the detected region. Features using linear transformations have the effect of dimension reduction as well as represent statistical characteristics, and show the robustness in translation and rotation of objects. Among the linear transformations, the NMF(Non-negative Matrix Factorization) is one of part-based representation. Therefore, we can extract NMF features with sparsity and improve the vehicle recognition rate by the representation of local features of a car as a basis vector. In this paper, we propose a feature extraction using NMF suitable for the vehicle recognition, and verify the recognition rate with it. Also, we compared the vehicle recognition rate for the occluded area using the SNMF(sparse NMF) which has basis vectors with constraint and LVQ2 neural network. We showed that the feature through the proposed NMF is robust in the urban scene where occlusions are frequently occur.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.1
• /
• pp.25-30
• /
• 2017

Millimeter wave (mmWave) bands are expected to improve date rate of 5G systems due to the wide available bandwidth. While severe path loss in those bands has impeded the utilization, short wavelength enables a large number of antennas packed in a compact form, which can mitigate the path loss. However, estimating the channel with a conventional scheme requires a huge training overhead, hence an efficient estimation scheme operating with a small overhead needs to be developed. The sparsity of mmWave channels caused by the limited scatterers can be exploited to reduce the overhead by utilizing compressed sensing. In this paper, we introduce compressed sensing techniques for mmWave channel estimation. First, we formulate wideband channel estimation into a sparse recovery problem. We also analyze the characteristics of random measurement matrix constructed using quantized phase shifters in terms of mutual incoherence.

• Journal of Korea Water Resources Association
• /
• v.31 no.4
• /
• pp.439-448
• /
• 1998

There are many methods to calculate steady-state flowrate in a large water distribution system. Linear method which analyzes continuity equations and energy equations simultaneously is most widely used. Though it is theoretically simple, when it is applied to a practical water distribution system, it produces a very sparse coefficient matrix and most of its diagonal elements are to be zero. This sparsity characteristic of coefficient matrix makes it difficult to analyze pipe flow using the linear method. In this study, a graph theory is introduced to water distribution system analysis in order to prevent from producing ill-conditioned coefficient matrix and the technique is developed to produce positive-definite matrix. To test applicability of developed method, this method is applied to 22 pipes and 142 pipes system located nearby Taegu city. The results obtained from these applications show that the method can calculate flowrate effectively without failure in converage. Thus it is expected that the method can analyze steady state flowrate and pressure in pipe network systems efficiently. Keywords : pipe flow analysis, graph theory, linear method.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.4
• /
• pp.285-291
• /
• 2017

Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as an impulse response of the channel between the two signals. In this case, the characteristic of the channel has sparsity. Most of the existing methods do not take advantage of the channel sparseness. In this paper, we propose a time delay estimation method using BPD (Basis Pursuit Denoising) optimization technique, which is one of the sparse signal optimization methods, in order to utilize the channel sparseness. Compared with the existing GCC (Generalized Cross Correlation) method, adaptive eigen decomposition method and RZA-LMS (Reweighted Zero-Attracting Least Mean Square), the proposed method shows that it can mitigate the threshold phenomenon even under a white Gaussian source, a colored signal source and oceanic mammal sound source.

• Journal of Digital Convergence
• /
• v.13 no.1
• /
• pp.481-486
• /
• 2015

This study aimed to analyze how a variety of relations created by the actions of characters shown in SBS Wednesday and Thursday dramas are communicated by using Greimas' actantial model. This study analyzed the sparsity structure that the subject have in relations between the subject, and a desire structure that the subject have will appear if we look into the interactive and binding relation between the subject and the object in the process of solving those problems. In the relations between the subject and originator, the role of originator in the dramas was limited when his or her character is established as a poor character weaker than the subject and the one to be protected, whereas he or she was more authoritative than the subject in the structure that originator shares a role as an antagonist. Objectors shown in three works gave help to the solution of incident after all by turning coat as another cooperators of the subject and the object, showing a tendency to reach maturity rather than being unhappy.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.5
• /
• pp.61-69
• /
• 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 KIISE:Software and Applications
• /
• v.31 no.12
• /
• pp.1672-1683
• /
• 2004

Recommender system is a kind of web intelligence techniques to make a daily information filtering for people. Researchers have developed collaborative recommenders (social recommenders), content-based recommenders, and some hybrid systems. In this paper, we introduce a new hybrid recommender method - ICHM where clustering techniques have been applied to the item-based collaborative filtering framework. It provides a way to integrate the content information into the collaborative filtering, which contributes to not only reducing the sparsity of data set but also solving the cold start problem. Extensive experiments have been conducted on MovieLense data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.