Pareto Bid Estimation for Multi-Issue Bilateral Negotiation under User Preference Uncertainty

2021 ◽  
Author(s):  
Pallavi Bagga ◽  
Nicola Paoletti ◽  
Kostas Stathis
Keyword(s):  
User Preference ◽  
Bilateral Negotiation ◽  
Preference Uncertainty

An Exploratory Study of Ontology-Based Platform Analysis Under User Preference Uncertainty

2012 ◽  
Author(s):  
Soon Chong Johnson Lim ◽  
Ying Liu ◽  
Han Tong Loh
Keyword(s):  
Bayesian Network ◽  
New Product Development ◽  
Product Family ◽  
User Preferences ◽  
User Preference ◽  
Knowledge Modeling ◽  
Laptop Computer ◽  
Preference Uncertainty ◽  
Network Representation ◽  
Customer Preferences

Analysis of user preference is among the crucial tasks at early stages of new product development (NPD). In order to satisfy diversified user preferences in the market, product companies have struggled to design a variety of products to address different customer voices. In this context, product family design (PFD) is a widely adopted strategy to deal with such product realization needs. Besides preference diversity, uncertainty of user preference is another important aspect that can greatly affect product design and offerings especially when customer preferences are not clear, not fully identified, or have drifted overtime. Previously, we have studied an ontology-based information representation for PFD, which offers a modeling scheme to assist multi-faceted product variant derivation. In this paper, we explore how ontology can be further extended to handle user preference uncertainty by using a Bayesian network representation. Customer preference uncertainty is expressed as a probability of preference towards certain product attributes. An approach to construct a Bayesian network that harnesses the existing knowledge modeling from product family ontology is proposed. Based on such a network representation and preference modeling, we have derived several probabilistic measures to assess the propagation and impact of user preference uncertainty towards platform preference. A case study of platform analysis using four laptop computer families is reported to illustrate how preference uncertainty can affect the suitability and selection of existing product platform.

Analysis of athletes’ stadium stress source based on improved layered K-means algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong
Keyword(s):  
Experimental Data ◽  
Machine Learning Algorithms ◽  
User Preference ◽  
Experimental Comparison ◽  
Actual Situation ◽  
Medical Field ◽  
Pressure Source ◽  
Preference Model ◽  
Related Research ◽  
Analysis System

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

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