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This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

Prefabricated construction is an innovative technique for decreasing carbon emissions in the construction industry. However, as the investors of housing projects, a majority of developers are unwilling to adopt prefabricated housing in practice. To promote prefabricated housing, this study aims to develop an integrated framework of fuzzy-decision making trial and evaluation laboratory (fuzzy-DEMATEL) and system dynamics (SD) to understand the underlying influencing mechanism of developers' willingness.

Through literature review, a total of 17 influencing factors were identified. Then, the interrelationships among the factors were evaluated by 10 experienced professionals, and the impacts given and received by each factor were further analyzed through fuzzy-DEMATEL. Based on the technology acceptance model (TAM), a SD model was developed to explore the influencing mechanism.

The major cause factors were identified, including mandatory implementation policies, economic incentive policies, environmental protection policies, component standardization and developers' economic strength. This group of factors was expected to be given priority attention in the case of limited resources. On the other hand, the results indicated that economic incentive policies and mandatory implementation policies could affect the developers' willingness via perceived usefulness, while the others mainly influenced perceived ease of use.

Little research has focused on the interrelationships among the influencing factors of developers' willingness to adopt prefabricated housing. This study contributed to understanding the mechanism of developers' willingness from a systematic view and providing the priority of influencing factors. Several strategies were proposed to improve the practical implementation of prefabricated housing development.