• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.31-36
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• 2012

In recent years, there has been increasing interest in recommender systems which provide users with personalized suggestions for products or services. In particular, researches of collaborative filtering analyzing relations between users and items has become more active because of the Netflix Prize competition. This paper presents the reinforcement learning approach for collaborative filtering. By applying reinforcement learning techniques to the movie rating, we discovered the connection between a time sequence of past ratings and current ratings. For this, we first formulated the collaborative filtering problem as a Markov Decision Process. And then we trained the learning model which reflects the connection between the time sequence of past ratings and current ratings using Q-learning. The experimental results indicate that there is a significant effect on current ratings by the time sequence of past ratings.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.3
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• pp.113-124
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• 2018

The objective of this study is to examine the effectiveness of signals and advancing our understanding of the relationship between ratings and audience decisions based on the signaling theory. Though many studies argue that information asymmetry affects decision making, few studies have examined two key signaling factors: its potential to have multiple sources and the effect of time on its effectiveness. This study examined how experts' and the general audience's ratings affect decision making. We also considered change patterns in ratings to explore how time effect on ratings affect selection behavior. We tested our hypotheses using the latent growth model based on signaling theory and behavior approaches. The results show that a general audience's ratings is perceived as more credible than are those of experts and that audience members are significantly affected by upward patterns in ratings. The findings suggest that general audiences play a critical role as signal providers. Thus, market participants such as producers should pay more attention to the general audience's ratings in order to increase revenues. They should also consider the time effect of signaling, such as upward trends in ratings.

• Journal of Engineering Education Research
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• v.19 no.3
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• pp.35-43
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• 2016

The purpose of this study is to explore directions for improvement of teaching at college of engineering based on analysis of differences from course evaluation of students and instructors. Data was collected from 86 instructors' ratings on courses and their 3004 students' ratings on courses at college of engineering in a two-year, a three-year college and a University from 2010 to 2013. The results of the survey indicate significant differences in the statistics from the several questions between the instructors and the students as well as between the course in a two-year, a three-year college and in a University. First, instructors' self evaluation of the course is higher than students' satisfaction ratings of the course on the average. Instructors' self evaluation are high on the questions 'The subject was proper for the course', 'The course provided the latest theory and trend of the field', and 'Fairness and objectivity about the exams and the assignments'. Also, the difference between Instructors and students on the questions is significant in the statistics. The professor must make sure that students know well how to organize the course content and the method for feedback to test result and homework. Second, instructors have higher satisfaction ratings on the six questions and students have higher satisfaction ratings on the one question('Make students participate in the class effectively') at a two-year and a three-year college. However, students have higher satisfaction ratings on the three questions('Make students participate in the class effectively', 'Concern about students' learning process', and 'Use of E-learning and media equipments') and instructors have higher satisfaction ratings on the one question. It means instructors at a University feel pressure on a teaching and they are unsatisfied with their teaching skills. Third, the result of comparing six parts of the questions shows that students' satisfaction ratings are higher on 'Students participation' and 'Application of media equipments' parts whereas instructors' self evaluation are higher on 'Exams and assignments' part. Fourth, the question 'Make students participate in the class effectively' is significant in statistic based on comparison of instructors and students, and comparison of in a college and a University. Students' satisfaction ratings are higher than instructors' self evaluation.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.3
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• pp.560-568
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• 2001

The aim of the present study was to measure intra-individual consistency in clothing and fabric evaluation and to examine its relation to the ratings. A sample of 93 female and 97 male university students rated clothing of 4 styles of daytime wear and 2 fabrics on 15 pairs of polar adjectives twice in 7-days interval. Correlation coefficients between the two ratings for each subject, intra-individual consistency in the evaluation, ranged from -0.12 to 0.89 and mean coefficient was 0.63 of female and -0.01 to 0.78 and mean coefficient was 0.54 of male. Based on the coefficients, the subjects were classified into three groups: high, medium, and low intra-individual consistency. Analysis of variance of mean ratings by the three groups revealed that significant difference existed in 24% of female and 23% of male in 90 combinations of 6 clothing and 15 semantic differential scales. Female of subjects with high intra-individual consistency were most likely definite to evaluate clothing, whereas the ones with low were least. But male subjects were not definite. Mean correlation coefficients for style evaluation subscales of female was 0.39, but male was 0.44. Among the semantic differential scales, high stability in the two ratings was observed for the synthetic clothing evaluation. Correlation coefficients for each clothing obtained from the mean score of the subjects in each semantics differential scale were around 0.98, including that the mean scores of the subjects in each scale could yield excellent stability in clothing evaluation.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.5
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• pp.37-44
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• 2010

Impact noise is induced in a car when it is driven on a harsh road or over some bumps. This noise occurs with the very high level of sound, which affects passengers in some way or other. Although it is impossible to clearly remove such noise, it is necessary to research an improvement in sound quality for impact noise. A new sound metric for impact sound is presented. This metric is verified by comparison between mean subjective ratings and several sound metrics. In this paper, more objective attributes are considered, which the attributes are expressing the level and modulation of sound. Three sound metrics are employed to get impact sound indexes for each course by the method of multiple linear regressions. The indexes are verified by considering the correlation between the estimated values from the multiple linear regressions and the mean subjective ratings by evaluators. Also, the subjective ratings on the indexes are estimated for the case in which some parts of suspension system are changed. The estimated ratings represent more reasonable or acceptable ratings. Thus, such indexes can be used for modification of the parts of suspension system under considering a good sound quality.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.25-32
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• 2019

In the Korean capital market, there are three credit rating agencies. Potential credit ratings based on credibility in the financial market are calculated independently for each rating agency. It often happens that despite the fact that the grades of the rating agencies are the same and have the same rating system, their actual ratings are different, even for the same firm. In such circumstances, investors may wonder why. In this study, we assume that the cause is the information environment in which the company operates. The credit ratings of rating agencies are mainly classified into bonds or commercial papers. The bonds are rated primarily for long-term of three years or more, and commercial papers specify ratings for less than one year. The information environment to be verified in this study was observed with a commercial paper. Under the assumption the larger the analyst following is, the more transparent is the information environment, we analyzed the influence of the number of analysts following on the degree to which ratings conflicted among credit rating agencies. The results of our analysis confirmed that opinion conflict among credit rating agencies is clearly reduced for companies with good information environments.

• Journal of Korean Academy of Nursing
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• v.49 no.2
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• pp.161-170
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• 2019

Background: Quality ratings could provide vital information to help people in choosing a nursing home. Purpose: This study investigated factors aligned with quality ratings of nursing homes. Methods: We employed a cross-sectional descriptive design to assess publicly available data on 1,354 nursing homes with 30 or more beds in the Republic of Korea. After excluding 289 nursing homes with no reported quality-evaluation ratings, we analyzed the 2015 data of 1,065 nursing homes. To prevent multicollinearity among independent variables, we carefully selected the final set of variables based on clinical and theoretical meaningfulness to direct nursing care. Quality, the ordinal outcome, was scored from 1 to 5 with a higher score indicating higher quality of the organization. We constructed a multivariate ordered logistic regression model. Results: Higher quality ratings of nursing homes was significantly related to the number of unoccupied beds (OR=0.99, p=.024), registered nurses (RNs) (OR=1.30, p=.003), qualified care workers (OR=1.03, p=.011), cognitive-improvement programs (OR=1.05, p=.024), and other programs for residents' activities (OR=1.09, p<.001). Conclusion: The number of RNs had the strongest influence on the publicly reported quality rating, while the rating of qualified care workers demonstrated little effect and that of nursing assistants had no effect. The number of RNs could be used as a crucial indicator for high-quality homes; more resident-engaging programs also demonstrated better quality of nursing home care.

• The Journal of Information Systems
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• v.32 no.3
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• pp.133-150
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• 2023

Purpose This study aims to develop classification models using a decision tree algorithm to identify core keywords and rules influencing online consumer review evaluations for the robot vacuum cleaner on Amazon.com. The difference from previous studies is that we analyze core keywords that affect the evaluation results by dividing the subjects that evaluate online consumer reviews into self-evaluation (star ratings) and peer evaluation (helpfulness votes). We investigate whether the core keywords influencing star ratings and helpfulness votes vary across different products and whether there is a similarity in the core keywords related to star ratings or helpfulness votes across all products. Design/methodology/approach We used random under-sampling to balance the dataset. We progressively removed independent variables based on decreasing importance through backwards elimination to evaluate the classification model's performance. As a result, we identified classification models that best predict star ratings and helpfulness votes for each product's online consumer reviews. Findings We have identified that the core keywords influencing self-evaluation and peer evaluation vary across different products, and even for the same model or features, the core keywords are not consistent. Therefore, companies' producers and marketing managers need to analyze the core keywords of each product to highlight the advantages and prepare customized strategies that compensate for the shortcomings.

• Management Science and Financial Engineering
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• v.9 no.2
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• pp.13-20
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• 2003

For inferring criteria in a performance appraisal. linear programming (LP) has been utilized as an alternative to policy capturing (PC). Previous policy capturing LP (PCLP) studies were limited to the criteria of exact numerical ratings. However. under certain evaluation circumstances, a ranged rating with a lower and upper bound may be preferred over an exact numerical value. Therefore, this study introduces a new LP model that allows ranged ratings. A simple example is given to illustrate our model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2948-2966
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• 2014

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies. We consider both user-based and item-based collaborative filtering approaches and propose novel privacy-preserving collaborative filtering schemes in this sense. We also evaluate our schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.