# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 2 Related Work  
There are plenty of works in the literature describing the overwhelming use of technology in heritage study and preservation in any aspect the reader can imagine [33; 34], and these technical advances have incorporated in more recent times a growing number of ICT technologies [35; 36; 37; 38], including the ones used in this work, that is, system recommenders [39] making extensive use of semantic approaches (LOD) [40; 41] and machine learning [42; 43]. However, much less effort has been dedicated to crowdsourcing initiatives in the field of CH [44; 45]. When developing crowdsourcing initiatives in the field of cultural heritage, it is not only important to fully understand the feasible contribution of humans in computation tasks but also the conveniences and disadvantages of software and tools adopted for the facilitation of this process and, increasingly importantly, the motivation of each specific individual. In 2005, The _Steve Museum_ project was one of the first projects launched to explore the crowdsourcing concept and encourage the public to contribute tags to some UK and US museum collections [46]. Tagging has been shown to provide a quite different vocabulary compared to museum official descriptions, with 86% of tags submitted not found in the museum documentation [47]. This was a proof-of-concept project with limited user functions and no support for linking to semantic vocabularies. However, in 2008 the Australian Newspapers Digitization Program can be considered the earliest large-scale initiative developed in this field to date, a project requiring the general public to review and correct a rather poor optical character recognition (OCR) text of millions of articles extracted from their database of historical newspapers. During the life of this successful project, more than 166 million text lines from newspaper articles were reviewed, corrected, or enhanced by volunteers working for the project [48]. In 2009, the _Waisda_? video labeling game was also released as a popular platform that used gaming to annotate television heritage [49], awarding points to players when their tags match some other one that their adversaries had already submitted within a certain time frame. _Waisda_? gameplay put a focus on responsiveness and accuracy, implicitly assuming that tags are genuine if there is widespread agreement among players; but still, the community itself acts as the filter, with no technological or interaction support to correct or improve the information quality. Furthermore, the European Union recently launched the _CrowdHeritage_ platform, an online crowdsourcing platform for enriching the metadata of digitized cultural heritage material available on the _European_ portal [50]. The website of this project allows users to leave comments on selected cultural archives or authenticate existing ones in the same basic way that people browse all existing catalogues with no customization provided. In 2014, some Swiss Heritage institutions were deeply studied in a complete survey to examine to what extent crowdsourcing and open data practices were present in their initiatives and routines [51], concluding that crowdsourcing and open data policies had been considered by very few institutions. However, there were some indications that several institutions might use these innovations in the near future, as the majority of the surveyed institutions deemed them important, considering that their opportunities overcome the identified risks. Regarding the Egyptian heritage, which is the testing scenery of this research, no documented crowdsourcing initiative was found in the literature. However, some Egyptian community-based initiatives have emerged with the goal of raising awareness about the ongoing loss of heritage, addressing acts of heritage damage, and seeking community support.