# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 1 Introduction
Research about motivating contributors across different types of crowdsourcing systems emphasizes the importance of intrinsic motivation, which occurs when contributors choose tasks that are inherently interesting or enjoyable rather than only extrinsic factors, such as payment [30; 31; 32]. This is to mean that the preference of people for concrete tasks is not solely determined by rewarding incentives set by the task requester. Instead, they rely heavily on the relationship of the characteristics of the task to the personal interests of the contributor. Therefore, CH crowdsourcing projects should pay more attention to targeting those people who might be especially interested in some types of assets, and they should be more concerned with engaging enthusiasts who can provide their contributions through the use of modern technologies. However, the main issue with most crowdsourcing initiatives is that items on CH platforms are assigned and displayed to all users at random without any distinctions or specific personalization, which frequently implies assigning users to annotation tasks that are far from their preferences and with their subsequent lack of interest. In this sense, the primary goal of the research presented in this paper is to assist interested contributors to find the best matching assets of CH platforms to annotate based on their personal interests as well as to help CH institutions make efficient use of the crowd to achieve higher quality annotations. The objective is to define and implement analysis strategies to identify such appealing assets for every contributor, discharging them from the arduous task of browsing the whole catalogue of the platform searching for the items most appropriate according to their knowhow or preferences. The premise of this research is that this quick access to the most motivating elements to contribute will improve the engagement of the crowd and the quality and amount of their contributions. To implement the results of this research line, an image annotation recommendation system for platform users has been developed, which was built leveraging technologies based on the semantic web, ontologies, word embeddings, and machine learning. In this context, we developed a model that classifies images on crowdsourcing CH platforms based on user features and recommends images to the user for annotation. As it is usually difficult to build accurate user profiles in these kinds of systems used sporadically, the approach of the work is to leverage the unstructured data stored in the social network of the contributors (with their agreement). These data, together with the textual data describing the CH platform assets, will be extended with tools and techniques of the Semantic Web field (using linked open data ontologies to discover more relevant features in them) and later processed with machine learning techniques and algorithms to achieve a structured representation and identify the most appropriate assets for each contributor. Obtaining structured data from the preceding methods was required to contribute to the development and evaluation of a classifier-based recommendation system that assists users in finding images that match their preferences and/or interests according to the features obtained or inferred for both of them. As described later, the system was tested and evaluated using three cutting-edge classifiers, including random forest, neural networks, and support vector machines. The restriction to image annotations is purely an implementation simplification to easily reach an appealing user interface to optimize user motivation. As the core of the algorithms are based in text descriptions accompanying the images (later semantically extended), simple descriptive characterizations of the CH items could also be offered to users. The rest of this paper is organized as follows. In Section 2, we present the related work regarding CH crowdsourcing initiatives. Section 3 details the materials and methods we exploit in the developed crowdsourcing recommendation system.