• Title, Summary, Keyword: auxiliary information

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Auxiliary Stacked Denoising Autoencoder based Collaborative Filtering Recommendation

  • Mu, Ruihui;Zeng, Xiaoqin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.6
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    • pp.2310-2332
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    • 2020
  • In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

Robust Bayesian Analysis in Finite Population Sampling with Auxiliary Information

  • Lee, Seung-A;Suh, Sang-Hyuck;Kim, Dal-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.4
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    • pp.1309-1317
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    • 2006
  • The paper considers some Bayes estimators of the finite population mean with auxiliary information under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. The proposed estimators are quite robust in general. Numerical methods of finding Bayes estimators under these heavy tailed priors are given, and are illustrated with an actual example.

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Robust Bayesian inference in finite population sampling with auxiliary information under balanced loss function

  • Kim, Eunyoung;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.3
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    • pp.685-696
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    • 2014
  • In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

A ZV-ZCT Boost Converter using an Auxiliary Resonant Circuit (보조 공진회로를 갖는 영전압-영전류 천이 부스트 컨버터)

  • Jung, Doo-Yong;Kim, Jun-Gu;Ryu, Dong-Kyun;Song, In-Beom;Jung, Yong-Chae;Won, Chung-Yuen
    • The Transactions of the Korean Institute of Power Electronics
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    • v.17 no.4
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    • pp.298-305
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    • 2012
  • This paper proposes a soft switching boost converter with an auxiliary resonant circuit. The auxiliary resonant circuit is added to a general boost converter and that is composed of one switch, one diode, one inductor and two capacitors. The resonant network helps the main switch to operate with a zero voltage switching(ZVS) and auxiliary switch also operates under the zero voltage and zero current conditions. The soft switching range is extended by the auxiliary switch and it is possible to control the proposed converter with a pulse width modulation(PWM). The ZVS and ZCS techniques make switching losses decreased and efficiency of the system improved. A theoretical analysis is verified through the simulation and experiment.

The Approximation for the Auxiliary Renewal Function (보조재생함수에 대한 근사)

  • Bae, Jong-Ho;Kim, Sung-Gon
    • The Korean Journal of Applied Statistics
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    • v.20 no.2
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    • pp.333-343
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    • 2007
  • The auxiliary renewal function has an important role in analyzng queues in which the either of the inter-arrival time and the service time of customers is not exponential. As like the renewal function, the auxiliary renewal function is hard to compute although it can be defined theoretically. In this paper, we suggest two approximations for auxiliary renewal function and compare the two with the true value of auxiliary renewal function which can be computed in some special cases.

Recalibration Estimation for Unit Nonresponse at the Two Levels Auxiliary Information

  • Yum, Joon Keun;Son, Chang Kyoon;Jeung, Young Mee
    • Communications for Statistical Applications and Methods
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    • v.10 no.3
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    • pp.665-678
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    • 2003
  • In this paper we suggest the new calibration estimator, which is called to the recalibration estimator, and its variance estimator using two-phase sampling technique according to the auxiliary information having strong correlation with the variable of interest under the unit nonresponse. In this unit nonresponse situation, an available information may exists at the level of whole population or the first-phase sample. The proposed recalibration estimator derives from the first and second phase weights respectively.

A Recommendation Model based on Character-level Deep Convolution Neural Network (문자 수준 딥 컨볼루션 신경망 기반 추천 모델)

  • Ji, JiaQi;Chung, Yeongjee
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.3
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    • pp.237-246
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    • 2019
  • In order to improve the accuracy of the rating prediction of the recommendation model, not only user-item rating data are used but also consider auxiliary information of item such as comments, tags, or descriptions. The traditional approaches use a word-level model of the bag-of-words for the auxiliary information. This model, however, cannot utilize the auxiliary information effectively, which leads to shallow understanding of auxiliary information. Convolution neural network (CNN) can capture and extract feature vector from auxiliary information effectively. Thus, this paper proposes character-level deep-Convolution Neural Network based matrix factorization (Char-DCNN-MF) that integrates deep CNN into matrix factorization for a novel recommendation model. Char-DCNN-MF can deeper understand auxiliary information and further enhance recommendation performance. Experiments are performed on three different real data sets, and the results show that Char-DCNN-MF performs significantly better than other comparative models.

Trust Analysis of Ground Resistance Measurements by the Substitute Auxiliary Electrode (대체 보조전극을 이용한 접지저항 측정 신뢰성 분석)

  • Lee, Sang-Ick;Yoo, Jae-Geun;Jeon, Jeong-Chay;Jeon, Hyun-Jae
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.56 no.2
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    • pp.109-114
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    • 2007
  • This paper summarize about the auxiliary electrode measured a ground resistance. The method to measure a ground resistance is the fall-of-potential method to using an auxiliary electrode. And an auxiliary electrode must be set up on the ground. Today it is so difficult to set up the auxiliary electrode on the ground because of many concrete building and many paved roads. So this paper is regarding a trust analysis of the ground resistance measurement by the substitute auxiliary electrode. It substituted a iron structure around the building, a neutral line multiplex ground to earth, a wire net for auxiliary electrode. This information is confirmed bv compared with the measurement value.

Robust Bayes and Empirical Bayes Analysis in Finite Population Sampling with Auxiliary Information

  • Kim, Dal-Ho
    • Journal of the Korean Statistical Society
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    • v.27 no.3
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    • pp.331-348
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    • 1998
  • In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

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A Study on Auxiliary Variable Selection in Unit Nonresponse Calibration (단위 무응답 보정에서 보조변수의 선택에 관한 연구)

  • 손창균;홍기학;이기성
    • The Korean Journal of Applied Statistics
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    • v.16 no.1
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    • pp.33-44
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    • 2003
  • Typically, it should be use auxiliary variable for calibrating the survey nonreponse in census or sampling survey. Where, if the dimension of auxiliary information is large, then it nay be spend a lot of computing time, and difficult to handle data set. Also because the variance estimator depends on the dimension of auxiliary variables, the variance estimator becomes underestimator. To deal with this problem, we propose the variable selection methods for calibration estimation procedure in unit nonreponse situation and we compare the efficiency by simulation study.