Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation

Chen, Tin-Chih; Chuang, Yu Hsuan

doi:10.1007/s12652-015-0335-2

Cited by 29 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a similar purpose, Sachdeva et al [39] applied the fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE) technique instead. Fuzzy measuring attractiveness by a categorical-based evaluation technique (MACBETH) methods are another type of fuzzy multiple-criteria decision-making method that has been widely applied [40,41]. The other is to adopt new types of fuzzy numbers.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Word Net-enabled k-means algorithm is used. Chen et al [29] examined a nonlinear and Fuzzy programming approach for the optimization of the performance of ubiquitous hotel recommendation system using hotels dataset. Fasahte et al [16] has explored the hotel recommendation system using Lexicon based approach to search sentiment exposure towards the hotel's aspects relied on the defined context.…”

Section: Recommender Systemsmentioning

confidence: 99%

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Ramzan

Bajwa

Jamil

et al. 2019

Scientific Programming

View full text Add to dashboard Cite

This paper presents an intelligent approach to handle heterogeneous and large-sized data using machine learning to generate true recommendations for the future customers. The Collaborative Filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type as additional information for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommend hotel class based on guest type using fuzzy rules. Different experiments are performed over the real world dataset obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated and results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.

show abstract

“…The model also addresses the Cold Start Problem for this domain [3]. In [4], a fuzzy and nonlinear programming approach for ubiquitous hotel recommendation has been proposed.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hotel Recommendation Model

Peng¹,

Shen²

2016

Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer

View full text Add to dashboard Cite

Abstract. With the development of Internet, a large amount of users purchase product, book hotels, buy goods from the web. They also share their experiences on the web. Other users can read the reviews or rating scores to choose the products or services. The user is difficult to read the information, since the data on the web is very huge. Recommendation system is an important technique which produces high quality recommendations. However, the amount of work increases with the number of participants in traditional recommendation system. In this paper, we propose a novel recommendation model which employs user-based collaborative filtering techniques to recognize the user-item matrix. Our experiment on different datasets shows that the proposed model acquires satisfactory performance than existing model.

show abstract

Fuzzy and nonlinear programming approach for optimizing the performance of ubiquitous hotel recommendation

Cited by 29 publications

References 25 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

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

A Novel Hotel Recommendation Model

Contact Info

Product

Resources

About