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The geographical location of Yazd province has significant potential for religious tourism. To make the most of this opportunity, it is important to develop an interpretive structural modeling. This paper aims to outline a model for the development of religious tourism in Yazd province as a Global Religious Destination by identifying and analyzing the interaction of effective drivers in future religious tourism development.

The research methodology is based on the interpretative paradigm and is developmental in nature. It takes an exploratory-analytical approach through the adoption of an inductive method and uses mixed research (qualitative/quantitative) involving religious tourism experts and tourists. The study consists of three main steps. In the first step, effective drivers in future religious tourism development were identified through content analysis of published articles. In the second step, the identified drivers were finalized through a survey of experts. Using futures research and Micmac software, a model was designed to investigate the interaction of the future drivers of religious tourism development. In the third step of the research, 384 tourists who visited Yazd Global Religious Destination participated to check the accuracy of the presented model.

Content analysis and review of scientific documents have identified 14 effective drivers in future religious tourism development that have finalized in a layered model. The model identified factors from the most influential at level one to the least influential at level eight. the theoretical foundations of this research confirmed by 384 visitors participated.

developing religious tourism, whether in the form of a religious city, a religious value system, a religious ceremony and a religious business, requires a comprehensive view that includes tourist’s mental and visual imagery, destination brand’s ideals and visions, religious belief, governmental and formal activities and the material and spiritual capital that can offer religious life style in the world markets to audiences from all over the world in the best way and in the dynamic conditions of competition between destination brands, occupy the first rungs of the audience’s mental ladder.

This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing.

For this purpose, 30,000 tags related to History on the LibraryThing have been selected. Their tags and the tags of the related recommended books were extracted from three different recommendations sections on LibraryThing. Then, four similarity criteria of Jaccard coefficient, Cosine similarity, Dice coefficient and Pearson correlation coefficient were used to calculate the similarity between the tags. To determine the most similar recommended section, the best similarity criterion had to be determined first. So, a researcher-made questionnaire was provided to History experts.

The results showed that the Jaccard coefficient, with a frequency of 32.81, is the best similarity criterion from the point of view of History experts. Besides, the degree of similarity in LibraryThing recommendations section according to this criterion is equal to 0.256, in the section of books with similar library subjects and classifications is 0.163 and in the Member recommendations section is 0.152. Based on the findings of this study, the LibraryThing recommendations section has succeeded in introducing the most similar books to the selected book compared to the other two sections.

To the best of the authors’ knowledge, itis for the first time, three sections of LibraryThing recommendations are compared by four different similarity criteria to show which sections would be more beneficial for the user browsing. The results showed that machine recommendations work better than humans.