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Passive design strategies contribute to improving indoor comfort conditions and reducing buildings' energy consumption. For several years, courtyards have received wide attention from researchers because of their significant role in reducing energy demand. However, the abundance of multi-story buildings and the courtyards' incompatibility with them, the courtyard is currently limited. Therefore, it is necessary to search for alternatives. This paper aims to bridge the gaps in previous limited studies considering skycourt as a passive alternative on the vertical plane of the facades in contrast to the courtyard.

This research presents an overview and a bibliometric analysis of the evolution of the courtyard to the skycourt via VOSviewer software and the bibliometrix R package.

The research provided various concepts related to skycourt as a promising passive design strategy, which can be suitable for multi-story buildings, starting with its evolution, characteristics, configurations, benefits, and challenges.

The findings can urge designers, researchers and policymakers to incorporate such an important passive alternative.

Researchers, instructors, educational specialists, faculty members, and decision-makers can provide design motivation for skycourt in buildings, in addition to achieving awareness about skycourt and its significant benefits and its role as an important passive design strategy.

The research highlights the possibilities of the skycourt and its role as a passive design element as an extension of the courtyard in addition to identifying design indicators that help designers determine the appropriate designs.

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.