# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 4 Crowdsourcing Recommendation System  
In this section, we describe how the system was developed to recommend images for annotations to users on CH crowdsourcing platforms. The users in the system represent the crowd, and the cultural platform is the CH organization that requests the image annotation task. As discussed previously, users on CH platforms volunteer their efforts and time to assist cultural organizations in the activities required; thus, CH platforms should make the user experience more appealing and intrinsically rewarding to avoid users leaving or becoming bored with participation.  
In fact, current CH platforms display all available collections for users in a similar manner, allowing them to select which images to annotate and provide descriptions for. With the vast number of images available on the platform, requiring the user to scroll and browse through them all to find interesting tasks to participate in may be very time-consuming and can lead to a negative user experience on such platforms, making users lose interest quite frequently. In other words, depending on the user's preferences, the probability of a match can be quite low and unsatisfactory, and we cannot rely on a simple browsing process to reduce the likelihood of user abandonment.  
Our recommendation system is intended to help users by suggesting images from the crowdsourcing platform that could be appealing to them for annotation rather than wasting their time browsing and looking for revealing choices for them, thereby improving their experience on such platforms and encouraging their engagement. The system is built around several classification technologies that support users in deciding which images to annotate because they are supposed to be related to their interests and/or preferences. The system development entails the following steps, which will be discussed in detail in the following subsections:  
1. Preparing datasets for system users as well as images available for annotation on the platform.
2. Retrieving extended structured data from the information extracted from various sources to be able to create user and image profiles.
3. Discovering semantic similarities between users and image data to guide the classification model used in the recommendation process.
4. Representing user and image features as well as training and testing them across different classification models to make recommendations.  
The overall system model is depicted in Figure 1, showing the processes and system users with the necessary interactions. In terms of crowdsourcing technologies, the users of the annotation platform represent the crowd, while the role of the requestor organization is played by the cultural institution that wants to enhance their assets' descriptions.  
The automatic recommendation system we have developed, aimed to suggest to users the images from the crowdsourcing platform most appropriate for them to annotate, uses several classification models to help users in making their decisions about what to annotate, and such a procedure is logically fed by several data sources containing a large amount of relevant information we have gathered to build and test the resulting framework. Finally, in the middle of these components, we can find the necessary algorithms and procedures to digitize and vectorize annotations about images and users to obtain the data format used by machine learning tools. In the following sections, all of this framework is detailed and described.