Integrating rich and heterogeneous information to design a ranking system for multiple products

Yang, Xian; Gao, Yang; Wu, Jiangning

doi:10.1016/j.dss.2016.02.009

Cited by 54 publications

(24 citation statements)

References 29 publications

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“…Quantitative criteria are those measured by numerical values which tend to be objective data and based on facts, while qualitative criteria are those measured by linguistic expressions. Motivated by the advantage of the heterogeneous information, it has been effectively used to address various types of decision-making problems, such as product ranking [16], cloud computing service evaluation [17], and supplier evaluation [18].…”

Section: Methodsmentioning

confidence: 99%

A Heterogeneous MCDM Framework for Sustainable Supplier Evaluation and Selection Based on the IVIF-TODIM Method

et al. 2019

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The third-party platform named ECO system is used by many transnational companies to monitor the sustainability performance of their global suppliers because of its easiness and shareability. Nonetheless, methods used in this platform for evaluating and calculating the sustainability performance of the alternative suppliers are criticized for their lack of accuracy. In response to these problems, this paper presents a heterogeneous multi-criteria decision-making (MCDM) method based on interval-valued intuitionistic fuzzy--an acronym in Portuguese for interactive multi-criteria decision making (IVIF--TODIM) to improve the efficiency of the evaluation model. Considering the varying features of evaluation criteria, i.e., either quantitative or qualitative, the evaluation values under different criteria are expressed in their appropriate information types. In this paper, a general method based on the relative closeness to the technique for order preference by similarity to ideal solution (TOPSIS) method is applied for aggregating the heterogeneous assessment information, including crisp numbers, interval numbers, and triangular fuzzy numbers (TFNs), into interval-valued intuitionistic fuzzy numbers (IVIFNs). Then, the TODIM (an acronym in Portuguese for interactive multi-criteria decision making) is extended and employed to prioritize the alternative suppliers. Finally, the applicability and effectiveness of the proposed method is verified by a practical example of polymer manufacturing company and a comparison analysis with existing methods.

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Section: Methodsmentioning

confidence: 99%

A Heterogeneous MCDM Framework for Sustainable Supplier Evaluation and Selection Based on the IVIF-TODIM Method

et al. 2019

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“…These online texts are valuable for the improvement of government, company and consumer decision making. Governments can make sounder public decisions by analyzing their citizens' online texts on social issues [3], companies can identify product weaknesses and forecast market demand by analyzing online product reviews [6,22,25] and consumers can make suitable purchasing decisions by identifying the sentiment orientation of a large number of online product reviews [18,46]. Several studies on opinion mining and sentiment analysis have been conducted to automatically mine the opinions embedded in the vast repository of online texts and analyze the sentiment classes of a massive number of online texts.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies on opinion mining and sentiment analysis have been conducted to automatically mine the opinions embedded in the vast repository of online texts and analyze the sentiment classes of a massive number of online texts. In these studies, sentiment classification is regarded as an important research topic for identifying the sentiment classes of online texts [6,18,22,25,46,48]. Over the past decade, dozens of studies have introduced methods or classifiers for sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

“…The sole consideration of positive and negative sentiment classes or online text orientations is regarded as excessive minimalism [34,39]. Multiple sentiment classes of online texts would enable more highly accurate and effective analyses for supporting the decisions of governments [3], companies [6,21,22,25] and consumers [18,46]. Thus, multi-class sentiment classification is a valuable research topic with extensive applications.…”

Section: Introductionmentioning

confidence: 99%

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A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

Liu

Fan

2017

Information Sciences

179

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Multi-class sentiment classification is a valuable research topic with extensive applications; however, studies in the field remain relatively scarce. In the present paper, a method for multiclass sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm is proposed. First, an improved OVO strategy is proposed wherein the relative competence weight of each binary classifier is determined according to the K nearest neighbors and the class center of each class in the training sample set concerning the binary classifier. A method for multi-class sentiment classification is proposed based on this improved OVO strategy and the SVM algorithm. After converting the training texts into term feature vectors, the important features (terms) for multi-class sentiment classification are selected using the information gain (IG) algorithm. A binary SVM classifier is then trained on the training feature vectors of each pair of sentiment classes. To identify the sentiment class of a test text, a confidence score matrix of multiple SVM classifiers is constructed based on the results of multiple SVM classifiers. Using this score matrix, the sentiment class of the test text can be determined using the improved OVO strategy. The results of our experimental studies show that the performance of the proposed method is significantly better than that of the existing methods for multi-class sentiment classification.

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“…To support consumer's selection and purchase decision, Fan et al designed a novel method based on stochastic dominance and PROMETHEE-II method to rank the alternative products by using online ratings [6]. Yang et al proposed a method to synthesize rich and heterogeneous information and further used it to rank products with Electronic Word of Mouth (eWOM) score [7]. Chen et al visualized the market structure from different perspectives and formulated a Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-based product selection model [8].…”

Section: Introductionmentioning

confidence: 99%

Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings

2018

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Abstract:With the remarkable promotion of e-commerce platforms, consumers increasingly prefer to purchase products online. Online ratings facilitate consumers to choose among products. Thus, to help consumers effectively select products, it is necessary to provide decision support methods for consumers to trade online. Considering the decision makers are bounded rational, this paper proposes a novel decision support model for product selection based on online ratings, in which the regret aversion behavior of consumers is formulated. Massive online ratings provided by experienced consumers for alternative products associated with several evaluation attributes are obtained by software finders. Then, the evaluations of alternative products in format of stochastic variables are conducted. To select a desirable alternative product, a novel method is introduced to calculate gain and loss degrees of each alternative over others. Considering the regret behavior of consumers in the product selection process, the regret and rejoice values of alternative products for consumer are computed to obtain the perceived utility values of alternative products. According to the prior order of the evaluation attributes provided by the consumer, the prior weights of attributes are determined based on the perceived utility values of alternative products. Furthermore, the overall perceived utility values of alternative products are obtained to generate a ranking result. Finally, a practical example from Zol.com.cn for tablet computer selection is used to demonstrate the feasibility and practically of the proposed model.

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Integrating rich and heterogeneous information to design a ranking system for multiple products

Cited by 54 publications

References 29 publications

A Heterogeneous MCDM Framework for Sustainable Supplier Evaluation and Selection Based on the IVIF-TODIM Method

A Heterogeneous MCDM Framework for Sustainable Supplier Evaluation and Selection Based on the IVIF-TODIM Method

A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

Selecting Products Considering the Regret Behavior of Consumer: A Decision Support Model Based on Online Ratings

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