# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 6 Conclusions and Future Work  
In the last decades, CH institutions have made a significant effort to introduce crowdsourcing technologies to increase the value of their online collections, encouraging the crowd to enhance the CH digital assets with their knowledge and experience. However, more research is necessary to address issues affecting the accessibility of users in CH environments. In this paper, we have presented a research work intended to build a recommendation system for the CH scenery, assisting users in finding the best artistic images for annotating based on a personalization approach, that is, considering their interests, preferences, and personal history to identify a collection of the most appropriate CH assets to them. The developed approach involves intelligent end-to-end processing of both images and user descriptions, starting with data gathering from different sources to test the system outside of a specific CH platform, mainly Facebook resources and Internet-free repositories of annotated images. Furthermore, the work revealed how semantic matchings using embeddings can compensate for the lack of data for testing the system. Moreover, the _DBpedia SpotLight_ tool was used to annotate the unstructured data, and DBpedia SPARQL endpoints were exploited to extend it with related DBpedia classes in order to find more relevant matches between users and images data. At the final stage, several well-known machine learning classification methods were used to identify the relevant assets to recommend (random forests, support vector machines, and neural networks). The data analysis derived from the experimental results indicates that the combination of semantic technologies and machine learning tools, like classification techniques, is a promising way to build recommendation systems that help to incorporate crowdsourcing approaches in the enhancement of CH asset annotations. The automatic recommendation of the most appropriate images for each user, matching their interests, expertise, and preferences, seems undoubtedly a more promising approach to engage them in the crowdsourcing task, rather than a sometimes laborious and fruitless search through the vast collections of CH institutions. Our work revolves around existing well-known methods and tools, but we were able to apply them in different ways other than those traditionally used in the literature. On one hand, our results show that using word embedding techniques to enhance processes of semantic matching is an approach that deserves attention when creating synthetic data in scenarios with scarce datasets. Additionally, the work emphasized the benefits of utilizing the vast information of social media that users contribute, as well as their engagements on various pages and groups, to create profiles and users' datasets. Furthermore, the research shows the benefits of using the huge amount of structured information contained in the DBpedia ontology to identify features related to the original data for both assets and users, that is, to discover hidden links between them that can be used to enhance the matching process from a semantic point of view, resulting in better classification results. Finally, while crowdsourcing for cultural heritage preservation is a growing demand that has been addressed by large cultural organizations, there is a growing need to explore new technologies and develop such customized recommendation systems in order to capitalize on the best opportunity to achieve better quality from user participation and as well provide users with satisfactory experiences. As future evolutions of the presented research, we can identify several lines focused on different aspects of the developed system, ranging from using other intelligent technologies to build the task recommendation algorithm to exploring different target scenarios to apply a similar crowdsourcing approach.