A multi-criteria decision making approach for recommending a product using sentiment analysis

Kumar, Gaurav

doi:10.1109/rcis.2018.8406679

Cited by 15 publications

(10 citation statements)

References 22 publications

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“…The North American school proposes the following: The Analytic Hierarchy Process (AHP) [ 54 ] combines conflicting physical and psychological elements based on appraisals and assessments to manage complex decisions. In [ 42 , 43 ], decision-making is handled under uncertainty, while in [ 44 ], it is based on subjective product recommendations from consumers. …”

Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

View full text Add to dashboard Cite

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“…-Analytic Hierarchy Process (AHP): this method is structured to manage complex decisions based on appraisals and assessments, combining physical and psychological elements which are usually conflicting. In studies such as [25], this is evident because decision making is tackled under uncertainty or based on subjective product recommendations from consumers [21].…”

Section: Multiple Criteria Decision Makingmentioning

confidence: 99%

Decision Making Under Uncertainty for the Deployment of Future Networks in IoT Scenarios

Alzate-Mejía

Boada

Amazonas

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The main characteristic of various emerging communication network paradigms in the dimensioning, control and deployment of future networks is the fact that they are human-centric, entailing closelyknit interactions between telematics and human activities. Considering the effect of user behavior, whose dynamics are difficult to model, new uncertainties are introduced in these systems, bringing about network resource management challenges. Within this context, this study seeks to review different decision-making computational methods in conditions of uncertainty for Internet of Things scenarios such as smart spaces, and industry 4.0, through a systematic literature review. According to our research results, a new paradigm for computationally capturing and modeling human behavior context must be developed with the purpose of improving resource management.

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“…Although such single rating-based approaches show a smooth and satisfying performance, as the appearance of multi-criteria recommendation techniques, one has been perceived that single criteria systems have relatively less accurate [18,5,28]. Thus, many researchers have studied to propose a new model for MCRSs [4,30,17,27].…”

Section: Related Workmentioning

confidence: 99%

Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

Hong

Jung

2021

COMSIS J

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Multi-Criteria Recommender Systems (MCRSs) have been developed to improve the accuracy of single-criterion rating-based recommender systems that could not express and reflect users? fine-grained rating behaviors. In most MCRSs, new users are asked to express their preferences on multi-criteria of items, to address the cold-start problem. However, some of the users? preferences collected are usually not complete due to users? cognitive limitation and/or unfamiliarity on item domains, which is called ?partial preferences?. The fundamental challenge and then negatively affects to accurately recommend items according to users? preferences through MCRSs. In this paper, we propose a Hypothetical Tensor Model (HTM) to leverage auxiliary data complemented through three intuitive rules dealing with user?s unfamiliarity. First, we find four patterns of partial preferences that are caused by users? unfamiliarity. And then the rules are defined by considering relationships between multi-criteria. Lastly, complemented preferences are modeled by a tensor to maintain an inherent structure of and correlations between the multi-criteria. Experiments on a TripAdvisor dataset showed that HTM improves MSE performances from 40 to 47% by comparing with other baseline methods. In particular, effectivenesses of each rule regarding multi-criteria on HTM are clearly revealed.

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A multi-criteria decision making approach for recommending a product using sentiment analysis

Cited by 15 publications

References 22 publications

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision Making Under Uncertainty for the Deployment of Future Networks in IoT Scenarios

Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

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