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Multi-criteria recommender systems (MC-RSs) allow users to express their preference in multiple aspects. Bayesian flexible mixture model (BFMM) is a model-based RS which extends FMM from single-criterion to MC. However, results of BFMM have a preference on different rating pattern problem. In single-criterion, FMM with decoupled normalization and W’s transposed function try to solve this problem. However, these techniques are applied to each criterion separately. Then, the relationship among criteria will be lost. This paper aims to solve different rating pattern problems and loss of the relationship between criteria.

The proposed method is combining between BFMM and rating conversion. First, mean and variance normalization is applied to make MC ratings of an active user and a neighbor lying on the same plane. After that, a pattern of each user is extracted using principal component analysis (PCA). Next, the pattern is used to convert neighbors’ MC ratings to the active user aspect. After that, converted MC ratings of neighbors are aggregated to be overall ratings using multiple linear regression (MLR). Finally, overall rating of the active user toward the target item is predicted using weighted average on the derived neighbors’ overall ratings where the similarity from BFMM acts as a weight.

The experimental results show that the proposed method where all criteria ratings are converted simultaneously can improve the performance of recommendation.

The proposed method predicts overall rating of the active user by converting MC ratings of each neighbor to the active user aspect at the same time, which can reduce the loss of the relationship between criteria.

Transportation project evaluation and prioritization use traditional performance measures including travel time, safety, user costs, economic efficiency and environmental quality. The project impacts in terms of enhancing the infrastructure resilience or mitigating the consequences of infrastructure damage in the event of disaster occurrence are rarely considered in project evaluation. This paper aims to present a methodology to address this issue so that in prioritizing investments, infrastructure with low security can receive the attention they deserve. Second, the methodology can be used for prioritizing candidate investments from a budget that is dedicated specifically to security enhancement.

In defining security as the absence of risk of damage from threats due to inherent structural or functional resilience, this paper uses security-related considerations in investment prioritization, thus introducing robustness in such evaluation. As this leads to an increase in the number of performance criteria in the evaluation, the paper adopts a multi-criteria analysis approach. The paper’s methodology quantifies the overall security level for an infrastructure in terms of the threats it faces, its resilience to damage and the consequences in the event of the infrastructure damage.

The paper demonstrates that it is feasible to develop a security-related measure that can be used as a performance criterion in the evaluation of general transportation projects or projects dedicated specifically toward security improvement. Through a case study, the paper applies the methodology by measuring the risk (and hence, security) of each for multiple infrastructure assets. On the basis of the multiple types of impacts including risk impacts (i.e. increase in security) because of each candidate investment, the paper shows how to prioritize security investments across the multiple infrastructure assets using multi-criteria analysis.

The overall framework consists of the traditional steps in risk management, and the paper’s specific contribution is in the part of the framework that measures the risk. The paper shows how infrastructure security can be quantified and incorporated in the project evaluation process.

The decision-making to outsource and select the most suitable global manufacturing outsourcing partner (MOP) is complex and uncertain due to multiple conflicting qualitative and quantitative criteria as well as multiple alternatives. Vagueness and variability exist in ratings of criteria and alternatives by group of decision-makers (DMs). The paper provides a novel Stochastic Fuzzy (SF) method for evaluation and selection of agile and sustainable global MOP in uncertain and volatile business environment.

Four main selection criteria for global MOP selection were identified such as economic, agile, environmental and social criteria. Total 16 sub-criteria were selected. To consider the vagueness and variability in ratings by group of DMs, SF method using t-distribution or z-distribution was adopted. The criteria weights were determined using the Stochastic Fuzzy-CRiteria Importance Through Intercriteria Correlation (SF-CRITIC), while MOP selection was carried out using Stochastic Fuzzy-VIseKriterijumskaOptimizacija I KompromisnoResenje (SF-VIKOR) in the case study of footwear industry. Sensitivity analysis was performed to test the robustness of the proposed model. A comparative analysis of SF-VIKOR and VIKOR was made.

The worker’s wages and welfare, product price, product quality, green manufacturing process and collaboration with partners are the most important criteria for MOP selection. The MOP3 was found to be the best agile and sustainable global MOP for the footwear company. In sensitivity analysis, significance level is found to have important role in MOP ranking. Hence, the study concluded that integrated SF-CRITIC and SF-VIKOR is an improved method for MOP selection problem.

In a group decision-making, ambiguity, impreciseness and variability are found in relative ratings. Fuzzy variant Multi-Criteria Decision-Making methods cover impreciseness in ratings but not the variability. On the other hand, deterministic models do not cover either. Hence, the stochastic method based on the probability theory combining fuzzy theory is proposed to deal with decision-making problems in imprecise and uncertain environments. Most notably, the proposed model has novelty as it captures and reveals both the stochastic perspective and the fuzziness perspective in rating by group of DMs.

The proposed multi-criteria group decision-making model contributes to the sustainable and agile footwear supply chain management and will help the policymakers in selecting the best global MOP.

To the best of the authors’ knowledge, SF method has not been used to select MOP in the existing literature. For the first time, integrated SF-CRITIC and SF-VIKOR method were applied to select the best agile and sustainable MOP under uncertainty. Unlike other studies, this study considered agile criteria along with triple bottom line sustainable criteria for MOP selection. The novel method of SF assessment contributes to the literature and put forward the managerial implication for improving agility and sustainability of global manufacturing outsourcing in footwear industry.

The aim of this paper is to identify the criteria that are used for vendor or supplier rating, prioritize these criteria based on the industry inputs and develop a vendor rating model. The data were collected from an Indian start-up working in product development using three-dimensional printing (3DP).

Factors of importance for vendor rating were identified through industry visits, and interacting with the industry experts from the start-up under consideration, substantiated by extensive review of relevant literature. A questionnaire-based survey was carried out to further narrow down the factors important to the industry, prioritizing them with a pairwise comparison analysis as envisaged in the analytical hierarchy process (AHP) technique along with the calculation of consistency ratios. Technique for order preference by similarity to ideal solution (TOPSIS) methodology was further used for data aggregation.

This research brought forward the criteria that are useful for rating vendors or suppliers with reference to 3DP sector.

This paper integrates AHP and TOPSIS to solve a multi-criteria vendor rating problem. The attempt was made to make vendor rating process universal so that it can encompass all the vendors of the firm.

The purpose of this study is to suggest suitable movies for children among the various multimedia selections available these days. Multimedia have a significant impact on the social and psychological development of children who are often explored to inappropriate materials, including movies that are either accessible online or through other multimedia channels. Even though not all movies are bad, there are negative effects of offensive languages, violence and sexuality as exhibited in movies. Parents and guidance of children need all the help they can get to promote the healthy use of movies these days.

To offer parents appropriate movies of interest to their youths, the authors have developed MovRec, a personalized movie recommender for children, which is designed to provide educational and suitable entertaining opportunities for children. MovRec determines the appealingness of a movie for a particular user based on its children-appropriate score computed by using the backpropagation model, pre-defined category using latent Dirichlet allocation, its predicted rating using matrix factorization and sentiments based on its users’ reviews, which along with its like/dislike count and genres, yield the features considered by MovRec. MovRec combines these features by using the CombMNZ model to rank and recommend movies.

The performance evaluation of MovRec clearly demonstrates its effectiveness and its recommended movies are highly regarded by its users.

Unlike Amazon and other online movie recommendation systems, such as Common Sense Media, Internet Movie Database and TasteKid, MovRec is unique, as to the best of the authors’ knowledge, MovRec is the first personalized children movie recommender.

A GIS based environmental decision support system for municipal solid waste management under Indian socio‐economic and regulatory conditions, named as EDSS‐MSWI has been developed. This paper intends to report the methodology and application of the EDSS‐MSWI for municipal landfill site selection taking a case study of Varanasi city (India).

EDSS‐MSWI has been developed using VB.NET and ArcGIS Engine programming tools. A set of 13 criteria are selected for primary landfill site selection. Analytical hierarchy process (AHP) has been used to give weights to different criteria. The criteria were aggregated and suitability index (S) is generated using weighted linear combination (WLC) technique in GIS environment. The suitability index (0‐10) values are classified into four categories (Excluded, Less Preferable, Suitable and Best Suitable) to select the landfill site.

The results indicate that there are at least four locations under the “best suitable” category where adequate land seems available for landfilling in Varanasi. It is found that GIS based multi‐criteria decision analysis (MCDA) can be a powerful tool for such applications.

The weights of different criteria have been based on the opinion of three experts only. This may involve subjectivity to that extent. Aggregation of more experts’ views may further refine the values of weights.

The developed tool provides a helpful guide to solid waste management planners.

The development of EDSS‐MSWI is the first of its kind in India to consider all aspects of municipal solid waste management in a holistic way and present it in an independent software form.

The purpose of this paper is to present a hybrid multi-criteria decision-making (MCDM) method, by combining the AHP, ELECTRE II, ELECTRE III, ELECTRE IV and Copeland techniques for road maintenance prioritization, in which the roads are evaluated and ranked based on various criteria. The proposed method is applied to four streets in Tehran, as a case study.

First, a set of criteria for road maintenance was determined and their weights were obtained using the AHP method. Four streets in Tehran, Iran were considered as alternatives and prioritized using the ELECTRE II, ELECTRE III and ELECTRE IV methods. Finally, the results of employing the three methods were integrated using the Copeland method and a final result was obtained.

The findings of the study suggested that “road safety” is the most important criterion in maintenance and “traffic volume” and “pavement quality index (PCI)” have the second and third rank in importance. Moreover, “The width of the street” is the least important criterion in road maintenance. Additionally, the streets' final ranking was obtained using the proposed method.

The proposed method helps managers effectively assign their limited budget and resources to roads with higher maintenance priority and as the result, increase the roads efficiency.

In this research, eight main criteria were collected using previous researches and experts' opinions. Also, a new combination of different MCDM techniques is proposed in this research.

The purpose of this paper is to split loan customers to different credit ratings to ensure the results that show that customers with lower credit ratings have higher loss rates, and the number of customers that satisfies the bell-shaped distribution. Hence, the number of credit ratings, the distribution of the rated obligors among ratings can achieve a meaningful differentiation of risk, which can avoid the loan pricing confusion.

The authors introduce a multi-objective programming to establish the credit rating model. Objective function 1 minimizes the absolute difference between the obligor number proportion and perfect client proportion, following a standard normal distribution. Objective function 2 minimizes the total difference of the deviation between two adjacent credit ratings’ loss rates. This study combines the two objective functions to ensure the obligor number distribution and the monotonicity of the loss rate, and applies genetic algorithm to solve the model.

This study’s analysis is based on data from 6,155 enterprises, provided by a Chinese bank and Prosper P2P loan data. The empirical results reveal that the proposed approach can ensure the balance between both criteria and avoid undue concentration of obligors in particular grades.

The proposed credit model could help building a reasonable credit rating system, which is the prerequisite of loan pricing; thus, inaccurate credit rating can cause incorrect loss rate estimates and loan pricing.

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

In a multi-stakeholder recommender system (MSRS), stakeholders are the multiple entities (consumer, producer, system, etc.) benefited by the generated recommendations. Traditionally, the exclusive focus on only a single stakeholders' (for example, only consumer or end-user) preferences obscured the welfare of the others. Two major challenges are encountered while incorporating the multiple stakeholders' perspectives in MSRS: designing a dedicated utility function for each stakeholder and optimizing their utility without hurting others. This paper proposes multiple utility functions for different stakeholders and optimizes these functions for generating balanced, personalized recommendations for each stakeholder.

The proposed methodology considers four valid stakeholders user, producer, cast and recommender system from the multi-stakeholder recommender setting and builds dedicated utility functions. The utility function for users incorporates enhanced side-information-based similarity computation for utility count. Similarly, to improve the utility gain, the authors design new utility functions for producer, star-cast and system to incorporate long-tail and diverse items in the recommendation list. Next, to balance the utility gain and generate the trade-off recommendation solution, the authors perform the evolutionary optimization of the conflicting utility functions using NSGA-II. Experimental evaluation and comparison are conducted over three benchmark data sets.

The authors observed 19.70% of average enhancement in utility gain with improved mean precision, diversity and novelty. Exposure, hit, reach and target reach metrics are substantially improved.

A new approach considers four stakeholders simultaneously with their respective utility functions and establishes the trade-off recommendation solution between conflicting utilities of the stakeholders.