Understanding the determinants of satisfaction in P2P hosting is crucial, especially with the emergence of platforms such as Airbnb, which has become the largest platform for short-term rental accommodation. Although many studies have been carried out in this direction, there are still gaps to be filled, particularly with regard to the apprehension of customers taking into account their category. In this study, we took a machine learning-based approach to examine 100,000 customer reviews left on the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). However, the data collected do not give any information on the category to which the customer belongs to. So, we applied natural language processing (NLP) algorithms to the reviews in order to find clues that could help us segment them, and then we trained two regression models, multiple linear regression and support vector regression, in order to calculate the coefficients acting on each of the 6 elementary scores (precision, cleanliness, check-in, communication, location, and value) noted on Airbnb, taking into account the category of customers who evaluated the performance of their accommodation. The results suggest that customers are not equally interested in satisfaction metrics. In addition, disparities were noted for the same indicator depending on the category to which the client belongs to. In light of these results, we suggest that improvements be made to the rating system adopted by Airbnb to make it suitable for each category to which the client belongs to.

Mohamed Chiny

Omar Bencharef

Moulay Youssef Hadi

Younes Chihab

A Client-Centric Evaluation System to Evaluate Guest&#x2019;s Satisfaction on Airbnb Using Machine Learning and NLP

Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies&#x2013;Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score.

Thepparit Banditwattanawong

Masawee Masdisornchote

On Characterization of Norm-Referenced Achievement Grading Schemes toward Explainability and Selectability

One of the main economic issues in Iran is the issue of economic sanctions. These sanctions have been imposed by various institutions and countries around the world in various forms since 1979 against Iran. Economic sanctions have affected large sections of Iran&#x2019;s economy. Meanwhile, economic sanctions against Iran have had far-reaching effects on trade cycles in Iran. The purpose of this article is to investigate the impact of economic sanctions on the structure of business cycles in Iran. The sanction index is a tool for studying quantitative sanctions. The opinions of 15 experts in sanctions economics were collected using fuzzy questionnaires. And the sanction index was obtained. The fuzzy logic method in the MATLAB software space calculated the economic sanction index for 1979&#x2013;2019. The self-regression calculated the effect of economic sanctions on business cycles. There are two scenarios in this article. In scenario 1, sanctions increased inflation, reduced production, and reduced investment. Also, during the embargo period, the recessions are longer. The second scenario of the research shows the economy without sanctions. The results showed that, in these conditions, inflation has less effect on production and investment. And the economy will experience a long period of prosperity without sanctions.

M. I. Saeed Iranmanesh

A. Norallah Salehi

B. Seyyed Abdolmajid Jalaee

Using Fuzzy Logic Method to Investigate the Effect of Economic Sanctions on Business Cycles in the Islamic Republic of Iran

Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider&#x2019;s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms.

E. I. Elsedimy

Fahad Algarni

Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers

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