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This paper aims to find a way to personalize attraction recommendations for travelers. The research objective is to find a more accurate way to suggest new attractions to each traveler based on the opinions of other like-minded travelers and the traveler’s preferences.

To achieve the goal, developers have created a personalized system to generate attraction recommendations. The system considers an individual traveler’s preferences to construct a qualitative attraction ranking model. The new ranking model is the result of blending two processes: K-means clustering and the analytic hierarchy process (AHP).

The performance of the developed recommendation system has been assessed by measuring the accuracy and scalability of the ranking model of the system. The experimental results indicate that the ranking model always returns accurate results independent of the number of attractions and the number of travelers in each cluster. The ranking model has also proved to be scalable because the processing time is independent of the numbers of travelers. Additionally, the results reveal that the overall system usability is at a very satisfactory level.

The main theoretical implication is that integrating the processes of K-means and AHP techniques enables a new qualitative ranking model for personalized recommendations that deliver only high-quality attractions. However, the designed recommendation system has some limitations. First, it is necessary to manually update information about the new tourist attractions. Second, the overall response time depends on the internet bandwidth and latency.

This research contributes to the tourism business and individual travelers by introducing an accurate and scalable way to suggest new attractions to each traveler. The potential benefit includes possible increased revenue for travel agencies that offer personalized package tours and support individual travelers to make the final travel decisions. The designed system could also integrate with itinerary planning systems to plot out a journey that pinpoints what travelers will most enjoy.

This research proposes a design and implementation of a personalized recommendation system based on the qualitative attraction ranking model introduced in this article. The novel ranking model is designed and developed by integrating K-means and AHP techniques, which has proved to be accurate and scalable.

本研究主要探索如何建立个性化旅游胜地推荐模型。本研究通过分析旅游兴趣相似的游客意见和游客偏好选择, 建立一种更加准确推荐游客需要的旅游胜地方法。

为了达到研究目的, 本研究建立了一种个性化推荐旅游胜地的信息系统。其系统通过分析每个游客的旅游偏好来建设一种定性旅游胜地排名模型。这种新型模型主要通过结合以下两种分析算法：（1）K平均聚类算法（K-means clustering）（2）层次分析法（AHP）。

本研究建立的推荐信息系统经过了准确率和拓展性的测评。实验结果表明这种排名模型的准确率并不受旅游胜地多少和游客样本大小的影响。此外, 这种排名模型也具有拓展性, 因为算法时间并不受游客样本大小的影响。最后, 研究实验表明此排名模型客户体验性达到合格满意要求。

本研究的主要理论意义在于其结合了K平均聚类算法和层次分析法, 并建立了一种新型定性排名模型, 这种排名模型个性化地推荐更高质量的旅游胜地给游客。然而, 这种推荐信息系统有一些局限性。第一, 新旅游胜地的信息需要手动输入。第二, 整个系统的处理时间决定于网络带宽和延迟状况。

本研究的实践意义在于其建立了一种准确和具有拓展性的新型旅游胜地推荐模型。这种模型的潜在价值将有利于旅游机构提供定制化旅游套餐和帮助游客制定旅游计划。此外, 这种模型还可以结合旅游路线计划系统以制定更加使游客满意的旅游行程。

本研究推荐了一种基于定性旅游胜地排名模型的个性化旅游推荐模型。这种新型的排名模型结合K平均聚类算法和层次分析法, 实验证明这种模型更具准确性和拓展性。

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

In searching for a new budget formula for the College of Business at California State University at Long Beach, a major university in the west, several rational budget formulas were explored. This report develops an explanation in quantitative terms of the reasoning process pursued by the department chairs in arriving at the compromise budget allocation model that is currently in place in the college. It shows that in a group decision‐making process concerning the allocation of resources, compromises are made between decision‐makers in order to come to some common agreement, if one in fact exists. Rational models based on some formula are introduced, and resources can be allocated based on the formula. However, among the models presented using a decision matrix, the model that will eventually be selected is the one that has the minimal variance in ranking regrets and monetary regrets if the highest‐ranking model is not chosen. The ranking regret provides a good guide and quick identification of the “most‐likely‐to‐succeed” compromise model. However, the monetary regret appears to be the final compromise determinant.

Digital healthcare has become one of the most important Internet applications in the recent years, and digital platforms have been acting as interfaces between the patients and physicians. Although these technologies enhance patient convenience, they create new challenges in platform management. For instance, on physician rating websites, information overload negatively influences patients' decision-making in relation to selecting a physician. This scenario calls for an automated mechanism to provide real-time rankings of physicians. Motivated by an online healthcare platform, this study develops a method to deliver physician ranking on platforms by considering patients' browse behaviors and the capacities of service resources.

The authors use a probabilistic model for explicitly capturing the browse behaviors of patients. Since the large volume of information in digital systems makes it intractable to solve the dynamic ranking problem, we design a ranking with value approximation algorithm that combines a greedy ranking policy and the value function approximation methods.

The authors found that the approximation methods are quite effective in dealing with the ranking optimization on the digital healthcare system, and it is mainly because the authors incorporate the patient behaviors and patient availability in the model.

To the best of the authors’ knowledge, this is one of the first studies to present solutions to the dynamic physician ranking problem. The ranking algorithms can also help platforms improve system and operational performance.

The purpose of this paper is to present an inductive methodology, which supports ranking of entities. Methodology is based on Bayesian latent variable measurement modeling and makes use of assessment across composite indicators to assess internal and external model validity (uncertainty is used in lieu of validity). Proposed methodology is generic and it is demonstrated on a well‐known data set, related to the relative position of a country in a “doing business.”

The methodology is demonstrated using data from the World Banks' “Doing Business 2008” project. A Bayesian latent variable measurement model is developed and both internal and external model uncertainties are considered.

The methodology enables the quantification of model structure uncertainty through comparisons among competing models, nested or non‐nested using both an information theoretic approach and a Bayesian approach. Furthermore, it estimates the degree of uncertainty in the rankings of alternatives.

Analyses are restricted to first‐order Bayesian measurement models.

Overall, the presented methodology contributes to a better understanding of ranking efforts providing a useful tool for those who publish rankings to gain greater insights into the nature of the distinctions they disseminate.

Bradford distributions describe the relationship between ‘journal productivities’ and ‘journal rankings by productivity’. However, different ranking conventions exist, implying some ambiguity as to what the Bradford distribution ‘is’. A need accordingly arises for a standard ranking convention to assist comparisons between empirical data, and also comparisons between empirical data and theoretical models. Five ranking conventions are described including the one used originally by Bradford, along with suggested distinctions between ‘Bradford data set’, ‘Bradford distribution’, ‘Bradford graph’, ‘Bradford log graph’, ‘Bradford model’ and ‘Bradford’s Law‘. Constructions such as the Lotka distribution, Groos droop (generalised to accommodate growth as well as fall‐off in the Bradford log graph), Brookes hooks, and the slope and intercept of the Bradford log graph are clarified on this basis. Concepts or procedures questioned include: (1) ‘core journal’, from the Bradfordian viewpoint; (2) the use of traditional statistical inferential procedures applied to Bradford data; and (3) R(n) as a maximum (rather than median or mean) value at tied‐rank values.

The purpose of this research is to explore the objectivity and reliability of Entrepreneur Magazine's Franchise 500^® ranking system.

Using data from 1997 to 2004 rankings, regression analysis was used to determine the extent to which key variables explained the rank of franchise firms.

Several quantifiable measures, categorized by the publishers as “most important” or “important” to a firm's rankings, were found to have little or no explanatory power in the regression model. Longitudinal analysis revealed inconsistencies in the ranking among the top 100 ranked franchises, which question the ranking system's reliability.

Only a subset of the variables used to calculate the rankings are disclosed by the publisher, yet these variables explain a substantial portion of any given franchise's rank. Only the top 100 ranked firms were included in the study.

While considered to be important to a firm's rank, the amount of pending litigation and the percentage of terminations within the system, found to be indicators of conflict between franchisor and franchisees, appear to have little effect on a franchise's rank. Also, size of the franchise system appears to have a strong but inconsistent relationship with rank, both within any given year and over the time period covered by this study. Lastly, the relationship between growth rate and rank, another factor considered by the publisher to be most important, also appears inconsistent, both in terms of number of outlets added and percentage of growth attained over the previous year.

Due to the wide popularity of the ranking system by practitioners and researchers a more systematic examination of the ranking appears justified to understand the underlying research implications of franchising research as it relates to the Franchise 500.

The purpose of this paper is to investigate the possibility of including the cost consequence of failure in the a priori risk assessment methodology known as failure mode and effect analysis (FMEA).

A model of the standard costs that are incurred when an electronic control module in an automotive application fails in service was developed. These costs were related to the Design FMEA ranking of the level of severity of the failure mode and the probability of its occurrence. Monte Carlo simulations were conducted to establish the average costs expected for each level of severity at each level of occurrence. The results were aggregated using fuzzy utility sets into a nine-point ordinal scale of cost consequence. The criterion validity of this scale was assessed with warranty cost data derived from a case study.

It was found that the model slightly underestimated the warranty costs that accrued, but the fit could be improved with adjustments dictated by actual usage conditions.

Cost data used in the simulations were derived from government and academic surveys, analyses, and estimates of the manufacturing cost structure; and nominal costs for various quality issues experienced by Tier 2 automotive electronics supplier. Specificity is lacking. The sample size and the type of the failure modes used to validate the model are constrained by the number and type of products which have had demonstrable performance concerns over the past three years, with cost data available to the authors. The power of the validation is limited. The validation is considered a screening assessment.

This work relates the characterization of risk with its potential cost and develops a scaling instrument to allow the incorporation of cost consequence into an FMEA.

A ranking scale was developed that related severity and occurrence rank scores to a cost consequence rank that keys to a cost of quality figure (given as percent of sales) that would accompany a realization of the failure mode.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic.

A hybrid multicriteria model, i.e. Fuzzy Analytical Hierarchy Process (AHP), was used to assign weights to each criterion, which was subsequently analyzed by three approaches, namely Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory), and Evaluation Based on Distance from Average Solution (EDA), to rank the top ten companies in descending order of supply chain resilience. Further, sensitivity analysis is performed to identify the consistency in ranking with variation in weights. The rankings are validated by a novel Ensemble Ranking algorithm and by supply chain domain experts.

The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

“Crisis Management Beforehand” is most critical in the current pandemic scenario. This implies that companies need to first prioritize taking proactive steps in crisis management followed by the need to minimize the “Expected impact of pandemic.” Performance factors also need to be regulated (sales, supply chain rank and financial performance) to maintain the company's overall reputation. Considering the consistent performance of the China Energy Construction Group Tianjin Electric Power Construction Co., Ltd., it is recommended as the most reliable supply chain firm to forge strategic partnerships with other supply chain stakeholders like suppliers and customers. On the other hand, Bosch is not recommended as a supply chain reliable company and needs to improve its crisis management capabilities to minimize the pandemic impact.

The paper aims to identify the most supply chain resilient company suitable for the customized preferences of partner firms in the context of the Chinese supply chain framework during the COVID-19 pandemic. The rankings suggest the company “China Energy Construction Group Tianjin Electric Power Construction Co., Ltd” is the most feasible and resilient company, presenting interesting findings to partner firms, and Bosch is the least reliable supply chain company of the ten firms considered, thus presenting interesting findings to partner companies.

The purposes of this study were to measure the relevance status of Index Islamicus, evaluate the semantic correlation between a query and documents and inquire the basis of its rank. Sorting the retrieved results from the most relevant to the least relevant is the common option of an information retrieval system. This sorting mechanism or relevance judgment is computed by measuring closeness of query with its documents.

Forming up 100 queries on Islamic History and Civilizations, with two indexing elements (keyword and concept), a laboratory experiment was generated on its first ten items of the rank. Throughout an experimental research design, the relevance status value formula was used to measure system-computed rank and compare it with mean average precision.

The results showed that the average status value of Index Islamicus’s ranking on relevance criterion was 18 per cent effective in terms of retrieving precise documents. Despite the main focus of this study being only on one subject domain and the items calculated were only 1,000, this small percentage of its ranking mechanism proved that semantic correlations between queries with subject domain did not achieve the satisfactory level.

Implication of this study could be a guideline for further research on ranking mechanism of other search engines because the limitation of this study was Index Islamicus being the only database, which was the focus of this study.

Throughout this study, Index Islamicus would be benefited knowing the status of its ranking mechanism as well as other databases can make further research on their own ranking method following this study.

Researchers and vendors of online databases can ensure their users a true platform of search engine with a proper ranking list.

Relevance status value model for Index Islamicus on Islamic History and Civilization that allows the system to rank documents according to the match between document and query and gives the idea of a better index. The model improves the system’s ranking mechanism, and promotes the use of semantic relationships. This research promotes the computation of relevance status value by domain for capturing subject-specific relevance criteria and semantic relationships.