• Title/Summary/Keyword: Rating Prediction

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Evaluation of rating of railway bridge PSC beam by prediction of residual effective prestress force (잔류유효긴장력 추정에 의한 철도교 PSC Beam의 내하력 평가기법)

  • Lee Seong-Won;Lee Ki-Seong;Kim Hyeon-Gil
    • Proceedings of the KSR Conference
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    • 2005.11a
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    • pp.1203-1208
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    • 2005
  • This study is the evaluation of rating of railway prestressed concrete beam bridges by prediction of residual effective prestress force. Therefore, developed prediction method is based on the center camber of prestressed concrete beam, structural design report of various PSC beams, construction reference materials of PSC beams. Both rating evaluation and residual effective prestress force by developed method is compared with evaluation by structural design. This comparison results shows that this developed method is very effective method. Therefore evaluation of rating by prediction of residual effective prestress force will be used for evaluation of the rating of railway PSC beam bridges.

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Developing Medium-size Corporate Credit Rating Systems by the Integration of Financial Model and Non-financial Model (재무모형과 비재무모형을 통합한 중기업 신용평가시스템의 개발)

  • Park, Cheol-Soo
    • Journal of the Korea Safety Management & Science
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    • v.10 no.2
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    • pp.71-83
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    • 2008
  • Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.

Sentiment Analysis and Star Rating Prediction Based on Big Data Analysis of Online Reviews of Foreign Tourists Visiting Korea (방한 관광객의 온라인 리뷰에 대한 빅데이터 분석 기반의 감성분석 및 평점 예측모형)

  • Hong, Taeho
    • Knowledge Management Research
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    • v.23 no.1
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    • pp.187-201
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    • 2022
  • Online reviews written by tourists provide important information for the management and operation of the tourism industry. The star rating of online reviews is a simple quantitative evaluation of a product or service, but it is difficult to reflect the sincere attitude of tourists. There is also an issue; the star rating and review content are not matched. In this study, a star rating prediction model based on online review content was proposed to solve the discrepancy problem. We compared the differences in star ratings and sentiment by continent through sentiment analysis on tourist attractions and hotels written by foreign tourists who visited Korea. Variables were selected through TF-IDF vectorization and sentiment analysis results. Logit, artificial neural network, and SVM(Support Vector Machine) were used for the classification model, and artificial neural network and SVR(Support Vector regression) were applied for the rating prediction model. The online review rating prediction model proposed in this study could solve inconsistency problems and also could be applied even if when there is no star rating.

Multi-Class SVM+MTL for the Prediction of Corporate Credit Rating with Structured Data

  • Ren, Gang;Hong, Taeho;Park, YoungKi
    • Asia pacific journal of information systems
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    • v.25 no.3
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    • pp.579-596
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    • 2015
  • Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

CLASSIFICATION FUNCTIONS FOR EVALUATING THE PREDICTION PERFORMANCE IN COLLABORATIVE FILTERING RECOMMENDER SYSTEM

  • Lee, Seok-Jun;Lee, Hee-Choon;Chung, Young-Jun
    • Journal of applied mathematics & informatics
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    • v.28 no.1_2
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    • pp.439-450
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    • 2010
  • In this paper, we propose a new idea to evaluate the prediction accuracy of user's preference generated by memory-based collaborative filtering algorithm before prediction process in the recommender system. Our analysis results show the possibility of a pre-evaluation before the prediction process of users' preference of item's transaction on the web. Classification functions proposed in this study generate a user's rating pattern under certain conditions. In this research, we test whether classification functions select users who have lower prediction or higher prediction performance under collaborative filtering recommendation approach. The statistical test results will be based on the differences of the prediction accuracy of each user group which are classified by classification functions using the generative probability of specific rating. The characteristics of rating patterns of classified users will also be presented.

A Stepwise Rating Prediction Method for Recommender Systems (추천 시스템을 위한 단계적 평가치 예측 방안)

  • Lee, Soojung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.4
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    • pp.183-188
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    • 2021
  • Collaborative filtering based recommender systems are currently indispensable function of commercial systems in various fields, being a useful service by providing customized products that users will prefer. However, there is a high possibility that the prediction of preferrable products is inaccurate, when the user's rating data are insufficient. In order to overcome this drawback, this study suggests a stepwise method for prediction of product ratings. If the application conditions of the prediction method corresponding to each step are not satisfied, the method of the next step is applied. To evaluate the performance of the proposed method, experiments using a public dataset are conducted. As a result, our method significantly improves prediction and precision performance of collaborative filtering systems employing various conventional similarity measures and outperforms performance of the previous methods for solving rating data sparsity.

A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fuzzy System (협업적 필터링 및 퍼지시스템 기반 사용자 성향분석에 의한 영화평가 예측 시스템)

  • Lee, Soo-Jin;Jeon, Tae-Ryong;Baek, Gyeong-Dong;Kim, Sung-Shin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.2
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    • pp.242-247
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    • 2009
  • Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

The Effect of an Integrated Rating Prediction Method on Performance Improvement of Collaborative Filtering (통합 평가치 예측 방안의 협력 필터링 성능 개선 효과)

  • Lee, Soojung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.5
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    • pp.221-226
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    • 2021
  • Collaborative filtering based recommender systems recommend user-preferrable items based on rating history and are essential function for the current various commercial purposes. In order to determine items to recommend, prediction of preference score for unrated items is estimated based on similar rating history. Previous studies usually employ two methods individually, i.e., similar user based or similar item based ones. These methods have drawbacks of degrading prediction accuracy in case of sparse user ratings data or when having difficulty with finding similar users or items. This study suggests a new rating prediction method by integrating the two previous methods. The proposed method has the advantage of consulting more similar ratings, thus improving the recommendation quality. The experimental results reveal that our method significantly improve the performance of previous methods, in terms of prediction accuracy, relevance level of recommended items, and that of recommended item ranks with a sparse dataset. With a rather dense dataset, it outperforms the previous methods in terms of prediction accuracy and shows comparable results in other metrics.

Developing Corporate Credit Rating Models Using Business Failure Probability Map and Analytic Hierarchy Process (부도확률맵과 AHP를 이용한 기업 신용등급 산출모형의 개발)

  • Hong, Tae-Ho;Shin, Taek-Soo
    • The Journal of Information Systems
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    • v.16 no.3
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    • pp.1-20
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    • 2007
  • Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, this study presents a corporate credit rating method using business failure probability map(BFPM) and AHP(Analytic Hierarchy Process). The BFPM enables us to rate the credit of corporations according to business failure probability and data distribution or frequency on each credit rating level. Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the BFPM and the AHP model using both financial and non-financial information. Finally, the credit ratings of each firm are assigned by our proposed method. This method will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings.

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CNN Architecture Predicting Movie Rating from Audience's Reviews Written in Korean (한국어 관객 평가기반 영화 평점 예측 CNN 구조)

  • Kim, Hyungchan;Oh, Heung-Seon;Kim, Duksu
    • KIPS Transactions on Computer and Communication Systems
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    • v.9 no.1
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    • pp.17-24
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    • 2020
  • In this paper, we present a movie rating prediction architecture based on a convolutional neural network (CNN). Our prediction architecture extends TextCNN, a popular CNN-based architecture for sentence classification, in three aspects. First, character embeddings are utilized to cover many variants of words since reviews are short and not well-written linguistically. Second, the attention mechanism (i.e., squeeze-and-excitation) is adopted to focus on important features. Third, a scoring function is proposed to convert the output of an activation function to a review score in a certain range (1-10). We evaluated our prediction architecture on a movie review dataset and achieved a low MSE (e.g., 3.3841) compared with an existing method. It showed the superiority of our movie rating prediction architecture.