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Revenue maximization through improving click‐throughs is of great importance for price comparison shopping services (PCSSs) whose revenues directly depend on the number of click‐throughs of items in their itemsets. The purpose of this paper is to present an approach aiming to maximize the revenue of a PCSS by proposing effective itemset construction methods that can maximize the click‐throughs.

The authors suggest three itemset construction methods, namely naïve method (NM), exhaustive method (EM), and local update method (LM). Specifically, NM searches for the best itemset for an item in terms of textual similarity between an item and an itemset, while EM produces the best itemset for each item for maximizing click‐throughs by considering all the possible memberships of the item. Finally, through combining NM and EM, the authors propose an LM that attempts to improve click‐throughs by locally updating the memberships of items according to their ranks in each itemset.

Through evaluation of the proposed methods based on a real‐world dataset, it has been found that improvement of click‐throughs is small when itemsets are constructed by using the textual similarity alone. However, significant improvement in the number of click‐throughs was achieved when considering items' membership updates dynamically.

Unlike the previous studies that mainly focus on the textual similarity, the authors attempt to maximize the revenue through constructing itemsets that can result in more click‐throughs. By using the proposed methods, it is expected that PCSSs will be able to automatically construct itemsets that can maximize their revenues without the need for manual task.

Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.

In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.

The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.

This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.

Cross operation is a common operation method in the building construction process nowadays. Due to the crossover, each other's operations are disturbed, and risks also interact. This superimposed relationship of risks is worthy of attention. The study aims to develop a model for analyzing cross-working risks. This model can quantify the correlation of various risk factors.

The concept of cross operation and the cross types involved are clarified. The risk factors were extracted from cross-operation accidents. The association rule mining (ARM) was used to analyze the results of various cross-types accidents. With the help of visualization tools, the intensity distribution and correlation path of the relationship between each factor were obtained. A complete cross-operation risk analysis model was established.

The application of ARM method proves that there are obvious risk correlation deviations in different types of cross operations. A high-frequency risk common to all cross operations is on-site safety inspection and process supervision, but the subsequent problems are different. Cutting off the high-lift risk chain timely according to the results obtained by ARM can reduce or eliminate the danger of high-frequency risk factors.

This is the first systematic analysis of cross-work risk in the construction. The study determined the priority of risk management. The results contribute to targeted cross-work control to reduce accidents caused by cross-work.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

This article describes a service for providing literature recommendations, which is part of a networked digital library project whose principal goal is to develop technologies for supporting digital services. The proposed literature recommendation system makes use of the Web usage logs of a literature digital library. The recommendation framework consists of three sequential steps: data preparation of the Web usage log, discovery of article associations, and article recommendations. We discuss several design alternatives for conducting these steps. These alternatives are evaluated using the Web logs of our university’s electronic thesis and dissertation (ETD) system. The proposed literature recommendation system has been incorporated into our university’s ETD system, and is currently operational.

In 2013, Chinese president Xi Jinping proposed the concept of “One Belt and One Road” economic cooperation. “The Belt and Road Initiative (B&R)” is the short of “The Silk Road Economic Belt” and the “21st-century Maritime Silk Road,” which has got a series of remarkable achievements and worldwide attentions in past five years such as Asian Infrastructure Investment Bank, China–Pakistan Economic Corridor, B&R Forum for International Cooperation, etc. Especially, cross-border EC has greatly strengthened the trade links between countries along the way, which is a rare chance for Chinese Export-oriented Cross-border EC’s rapid growth. Thus, the authors take DHgate.com as a typical example to do a big data analysis. This chapter analyzes vast data from 2013 to 2017 about seven kinds of commodities including Fashion accessories, Jewelry, Sports & Outdoors, Security & Surveillances, Car accessories, Watches, and Hair & Styling by using data mining related software and algorithms. The authors do some monthly sale charts and find a few counter-intuitive but useful conclusions such as by taking association analysis, the study shows that sports products and jewelry products have strong association rules. In addition, for potential products (such as Fashion accessories and Jewelry), although their sales have a certain shock, the overall selling line keep rising. It is possible to put forward some practical suggestions for Chinese Export-oriented Cross-border EC that actively respond to the One Belt One Road Initiative based on these analysis results.

The purpose of this paper is to systematically develop a delay risk terminology and taxonomy. This research also explores two external and internal dimensions of the taxonomy to determine how much the taxonomy as a whole or combinations of its elements are generalisable.

Using mixed methods research, this systematic literature review incorporated data from 46 articles to establish delay risk terminology and taxonomy. Qualitative data of the top 10 delay risks identified in each article were coded based on the grounded theory and constant comparative analysis using a three-stage coding approach. Word frequency analysis and cross-tabulation were used to develop the terminology and taxonomy. Association rules within the taxonomy were also explored to define risk paths and to unmask associations among the risks.

In total, 26 delay risks were identified and grouped into ten categories to form the risk breakdown structure. The universal delay risks and other delay risks that are more or less depending on the project location were determined. Also, it is realized that delays connected to equipment, sub-contractors and design drawings are highly connected to project planning, finance and owner slow decision making, respectively.

The established terminology and taxonomy may be used in manual or automated risk management systems as a baseline for delay risk identification, management and communication. In addition, the association rules assist the risk management process by enabling mitigation of a combination of risks together.

This paper aims to improve operational efficiency and minimize accident frequency in cold storage facilities through adopting an effective occupational safety and health program. The hidden knowledge can be extracted from the warehousing operations to create the comfortable and safe workplace environment.

A fuzzy association rule-based knowledge management system is developed by integrating fuzzy association rule mining (FARM) and rule-based expert system (RES). FARM is used to extract hidden knowledge from real operations to establish the relationship between safety measurement, personal constitution and key performance index measurement. The extracted knowledge is then stored and adopted in the RES to establish an effective occupational and safety program. Afterwards, a case study is conducted to validate the performance of the proposed system.

The results indicate that the aforementioned relationship can be built in the form of IF-THEN rules. An appropriate safety and health program can be developed and applied to all workers, so that they can follow instructions to prevent cold induced injuries and also improve the productivity.

Because of the increasing public consciousness of occupational safety and health, it is important for the workers in cold storage facilities where the ambient temperature is at/below 10°C. The proposed system can address the social problem and promote the importance of occupational safety and health in the society.

This study contributes to the knowledge management system for improving the occupational safety and operational efficiency in the cold storage facilities.

In a large‐scale digital library, it is essential to recommend a small number of useful and related articles to users. In this paper, a literature recommendation framework for digital libraries is proposed that dynamically provides recommendations to an active user when browsing a new article. This framework extends our previous work that considers only Web usage data by utilizing content information of articles when making recommendations. Methods that make use of pure content data, pure Web usage data, and both content and usage data are developed and compared using the data collected from our university's electronic thesis and dissertation (ETD) system. The experimental results demonstrate that content data and usage data are complements of each other and hybrid methods that take into account of both types of information tend to achieve more accurate recommendations.

The purpose of this research is to identify the most impactful delay risks in Australian construction projects, including the associations amongst those risks as well as the project phases in which they are most likely present. The correlation between project and organisational characteristics with the impact of delay risks was also studied.

A questionnaire survey was used to collect data from 118 delayed construction projects in Australia. Data were analysed to rank the most impactful delay risks, their correlation to project and organisational characteristics and project phases where those risks are likely to emerge. Association rule learning was used to capture associations between the delay risks.

The top five most impactful delay risks in Australia were changes by the owner, slow decisions by the owner, preparation and approval of design drawings, underestimation of project complexity and unrealistic duration imposed to the project, respectively. There is a set of delay risks that are mutually associated with project complexity. In addition, while delay risks associated with resources most likely arise in the execution phase, stakeholder and process-related risks are more smoothly distributed along all the project phases.

This research for the first time investigated the impact of delay risks, associations amongst them and project phases in which they are likely to happen in the Australian context. Also, this research for the first time sheds light on the project phases for the individual project delay risks which aids the project managers to understand where to focus on during each phase of the project.