Multi-agent knowledge integration mechanism using particle swarm optimization

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.Unstructured group decision-making is burdened with several central difficulties: unifying the knowledge of multiple experts in an unbiased manner and computational inefficiencies. In addition, a proper means of storing such unified knowledge for later use has not yet been established. Storage difficulties stem from of the integration of the logic underlying multiple experts' decision-making processes and the structured quantification of the impact of each opinion on the final product. To address these difficulties, this paper proposes a novel approach called the multiple agent-based knowledge integration mechanism (MAKIM), in which a fuzzy cognitive map (FCM) is used as a knowledge representation and storage vehicle. In this approach, we use particle swarm optimization (PSO) to adjust causal relationships and causality coefficients from the perspective of global optimization. Once an optimized FCM is constructed an agent based model (ABM) is applied to the inference of the FCM to solve real world problem. The final aggregate knowledge is stored in FCM form and is used to produce proper inference results for other target problems. To test the validity of our approach, we applied MAKIM to a real-world group decision-making problem, an IT project risk assessment, and found MAKIM to be statistically robust.Ministry of Education, Science and Technology (Korea

BERT, SHAP, Kano 모델을 기반한 소비자 만족 요소 다이나믹스

학위논문(석사) -- 서울대학교대학원 : 경영대학 경영학과, 2022.2. 오정석 교수.최근 10년 간 온라인 쇼핑 산업의 성장으로 온라인 쇼핑몰 플랫폼에 온라인 리뷰 등 무한한 소비자 반응, 만족도 관련 정보가 생성되고 있다. 이에 많은 기업들과 학계에서 이를 활용하여 VoC (Voice of Customer)를 반영한 소비자 만족도 모델링을 시도하고 있다. 본 논문은 BERT, GBM, SHAP 등을 활용하여 카노 모델 (Kano Model)에 기반한 소비자 만족도 특성 (Customer Satisfaction Dimension)을 분류하고 각 특성의 소비자 요구 충족 여부가 소비자 만족도에 미치는 영향도를 측정한다. 본 논문의 방법론에 활용된 각 빅데이터 모델 성능과 선행 연구들에서 사용된 모델 성능을 직접 구현 및 비교하여, 본 논문에서 활용된 모델들의 정확성과 안정성을 보였다. 또한 해석적 머신러닝 기법인 SHAP를 도입하여, 카노 카테고리를 분류하는 통일된 분류 방식을 제안한다. 본 연구는 제시된 방법론을 통해 스마트폰 및 스마트워치 제품군을 대상으로 실증 연구를 진행하며, 산업계에 제품 개발 및 개선, 고객 세분화 전략 등 기업 의사결정 방향성에 유의미한 제언을 제시함으로써 본 방법론의 실용적 가치를 입증하였다.As a large number of online reviews are loaded on e-commerce platforms in recent days, companies are being able to measure customer satisfaction reflecting VoC (Voice of Customer) with big data analytics. This paper proposes the improved framework for identifying characteristics of customer satisfaction dimensions (CSD) based on Kano model using BERT (Bidirectional Encoder Representations from Transformers), GBM (Gradient Boosting Machine), and SHAP (Shapley Additive eXplanation). We proved each model outperformance by comparing other models which previous studies have used. And this paper suggests the unified rule of Kano model classification using SHAP. Furthermore, we conducted empirical studies regarding smartphone and smartwatch products which suggest the direction of product enhancement/development strategy and multi-product level customer segmentation strategy to product manufacturers. This shows proposed methodology’s effectiveness and usefulness on industrial analysis.1. Introduction 1 2. A framework for modelling customer satisfaction from online review 5 3. Research Method 8 3.1 Mining customer’s sentiments toward CSDs from online reviews 8 3.2 Measuring the effects of customer sentiments toward each CSD on customer satisfaction 11 3.3 Identifying the feature of each CSD from the customer’s perspective 11 3.4 Classifying each CSD into Kano categories 14 4. Empirical Study 17 4.1 Study 1 17 4.2 Study 2 24 5. Conclusion 27 6. Reference 29석

Enhancing Customer Satisfaction Analysis with a Machine Learning Approach: From a Perspective of Matching Customer Comment and Agent Note

With the booming of UGCs, customer comments are widely utilized in analyzing customer satisfaction. However, due to the characteristics of emotional expression, ambiguous semantics and short text, sentiment analysis with customer comments is easily biased and risky. This paper introduces another important UGC, i.e., agent notes, which not only effectively complements customer comment, but delivers professional details, which may enhance customer satisfaction analysis. Moreover, detecting the mismatch on aspects between these two UGCs may further help gain in-depth customer insights. This paper proposes a machine learning based matching analysis approach, namely CAMP, by which not only the semantics and sentiment in customer comments and agent notes can be sufficiently and comprehensively investigated, but the granular and fine-grained aspects could be detected. The CAMP approach can provide practical guidance for following-up service, and the automation can help speed-up service response, which essentially improves customer satisfaction and retains customer loyalty

Improving customer churn prediction by data augmentation using pictorial stimulus-choice data

The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures

Hotel online reviews: creating a multi-source aggregated index

Purpose This paper aims to develop a model to predict online review ratings from multiple sources, which can be used to detect fraudulent reviews and create proprietary rating indexes, or which can be used as a measure of selection in recommender systems. Design/methodology/approach This study applies machine learning and natural language processing approaches to combine features derived from the qualitative component of a review with the corresponding quantitative component and, therefore, generate a richer review rating. Findings Experiments were performed over a collection of hotel online reviews – written in English, Spanish and Portuguese – which shows a significant improvement over the previously reported results, and it not only demonstrates the scientific value of the approach but also strengthens the value of review prediction applications in the business environment. Originality/value This study shows the importance of building predictive models for revenue management and the application of the index generated by the model. It also demonstrates that, although difficult and challenging, it is possible to achieve valuable results in the application of text analysis across multiple languagesinfo:eu-repo/semantics/acceptedVersio

A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance

Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches.

In past two decades e-commerce platform developed exponentially, and with this advent, there came several challenges due to a vast amount of information. Customers not only buy products online but also get valuable information about a product they intend to buy through an online platform. Customers share their experiences by providing feedback which creates a pool of textual information and this process continuously generates data every day. The information provided by customers contains both subjective and objective text that contains a rich information regarding behaviour, liking and disliking towards a product and sentiments of customers. Moreover, this information can be helpful for the customers who are yet to buy or who are yet in decision making process. This thesis studies comparison of four supervised machine learning approaches to predict customer satisfaction. These approaches are: Naïve Bayes, Support Vector Machines (SVM), Logistic Regression (LR), and Decision Tree (DT). The models use term frequency inverse document frequency (TF-IDF) vectorization for training and testing sets of data. The models are applied after basic pre-processing of text data that includes the lower casing, lemmatization, the stop words removal, smileys removal, and digits removal. We compare the performance of models using accuracy, precision, recall, and F1-scores. Support Vector Machines (SVM) outperforms the rest of the models with the accuracy rate 83% while Naïve Bayes, Logistic Regression (LR) and Decision Tree (DT) have accuracy rate 82%, 78%, and 76%, respectively. Moreover, we evaluate the performance of classifiers using confusion matrix