Ubiquitous Hotel Recommendation Using a Fuzzy-Weighted-Average and Backpropagation-Network Approach

Chen, Toly

doi:10.1002/int.21863

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…One is to fuzzify an existing crisp multiple-criteria decision-making method by modelling the evaluation result of an alternative, the weight (or relative priority) of a criterion, and/or the weight (or authority level) of each decision maker with fuzzy numbers. For example, Chen [34] applied FWA to aggregate the performances of a hotel along various dimensions, and then defuzzified the aggregation result using a back propagation network. Similarly, fuzzy multi-attribute utility theory (MAUT) methods were applied to select intervention strategies to restore an aquatic ecosystem contaminated by radionuclides [35], assess intelligent buildings [36], and recommend suitable clinics to patients [37].…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

Wang

Chen

2020

Mathematics

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The COVID-19 pandemic has severely impacted our daily lives. For tackling the COVID-19 pandemic, various intervention strategies have been adopted by country (or city) governments around the world. However, whether an intervention strategy will be successful, acceptable, and cost-effective or not is still questionable. To address this issue, a varying partial consensus fuzzy collaborative intelligence approach is proposed in this study to assess an intervention strategy. In the varying partial consensus fuzzy collaborative intelligence approach, multiple decision makers express their judgments on the relative priorities of factors critical to an intervention strategy. If decision makers lack an overall consensus, the layered partial consensus approach is applied to aggregate their judgments for each critical factor. The number of decision makers that reach a partial consensus varies from a critical factor to another. Subsequently, the generalized fuzzy weighted assessment approach is proposed to evaluate the overall performance of an intervention strategy for tackling the COVID-19 pandemic. The proposed methodology has been applied to compare 15 existing intervention strategies for tackling the COVID-19 pandemic.

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Section: Literature Reviewmentioning

confidence: 99%

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

Wang

Chen

2020

Mathematics

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“…In today’s e-commerce, online providers often recommend proper goods or services to each consumer based on their personal opinions or preferences [ 21 ], [ 22 ]. However, it is a tough task to provide appropriate recommendation which may confront several difficulties.…”

Section: A Recommender System Integrating With Hierarchical Coarseninmentioning

confidence: 99%

A fast combination method in DSmT and its application to recommender system

Dong

Liu

2018

PLoS ONE

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In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time.

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“…In this era of information overload, recommender systems serve as a very useful information filtering framework that helps users to swiftly and correctly identify a resource or product from a rapidly growing set. [1][2][3] Over the years, several models have been developed to solve specific recommendation problems. In general, these recommendation models are classified into collaborative filtering (CF)-based recommender systems, [4][5][6] content-based (CB) recommender systems 7,8 and hybrid recommender systems.…”

Section: Introductionmentioning

confidence: 99%

Social‐trust‐aware variational recommendation

Walker

Zhang

et al. 2021

Int J of Intelligent Sys

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Most existing studies that employ social-trust information to solve the data sparsity issue in recommender systems assume that socially connected users have equal influence on each other. However, this assumption does not hold in practice since users and their friends may not have similar interests because social connections are multifaceted and exhibit heterogeneous strengths in different scenarios. Therefore, estimating the diverse levels of influence among entities (users/items/social connections) is very important in advancing social recommender systems. Towards this goal, we propose a new model named Social-Trust-Aware Variational Recommendation (SOAP-VAE). Particularly, SOAP-VAE leverages graph attention network techniques to capture the varying levels of influence and the complex interaction patterns among all the entities collectively and holistically. In doing so, heterogeneity among entities is obtained seamlessly. Consequently, we generate social-trustaware item embedding representations in which the right level of influence has been integrated. Next, based on these rich social-trust-aware item representations, we formulate the first-ever social-trust-aware prior in literature. Unlike priors utilized in earlier VAE-based recommendation models, this novel prior aids in dealing with the issue of posterior-collapse and can effectively capture the uncertainty of latent space. In effect, the model produces better latent representations, which significantly alleviates the data sparsity issue. Finally, we empirically show that SOAP-VAE outperforms several state-of-the-art baselines on three real-world data sets.

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Ubiquitous Hotel Recommendation Using a Fuzzy-Weighted-Average and Backpropagation-Network Approach

Cited by 12 publications

References 24 publications

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

Assessing and Comparing COVID-19 Intervention Strategies Using a Varying Partial Consensus Fuzzy Collaborative Intelligence Approach

A fast combination method in DSmT and its application to recommender system

Social‐trust‐aware variational recommendation

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