# Towards a Semantics-Based Recommendation System for Cultural Heritage Collections
## 2 Background and Related Work
### Semantic Recommendation  
Semantic recommendation is one of the prevalent Semantic Web applications, although it has not been widely used in the cultural heritage domain. The origin of recommendation systems, also known as recommender systems, can be traced back to the early 1990s when one such system was developed to filter personal emails [17]. At present, personalised recommendation systems have become ubiquitous from online shopping to streaming services. The basic principle of recommendation is that there are significant dependencies between user- and item-centric activity. For example, a user who is interested in historical documentaries is likely to be attracted to educational programmes rather than action movies [18]. The dependencies can be learned in a data-driven manner, and the resulting model is used to make predictions for target users [18]. A widely acknowledged operational goal of recommendation systems is to recommend items that are relevant, while the novelty, serendipity, and diversity of the recommended items are also considered as potentially desired properties [18].  
Recommendation systems are classified into three main types based on the recommendation method they use: collaborative, content-based, and knowledge-based. They work mainly with two kinds of data (1) the user-item interactions, such as ratings and browsing behaviours and (2) the profiles of users or items, which may include demographic information of users or search keywords [18; 19]. Collaborative filtering models focus on the first type of data. They generate recommendations based on the observed user-item correlations. In a user-based collaborative system, a user is likely to be recommended an item that other users related to that user have expressed interest in. In item-based collaborative filtering methods, a user receives recommendations for the same type of items that the user has spent time on [18]. Content-based and knowledge-based recommendation systems similarly make use of the attributes of items. Content-based filtering analyses attributes of items that are related to users' recorded activities, such as rating and browsing, and provides recommendations based on the users' preferences. A knowledge-based system recommends items according to explicit specifications provided by the users beforehand, such as constraints on the item attributes or sample cases as targets or anchor points [18].  
Semantically organised cultural heritage data suit recommendation systems, especially with content-based and knowledge-based methods [1; 19]. There have been some attempts at semantic recommendation on cultural heritage portals. [PERSON] and [PERSON] [20] proposed a method for determining the semantic relevance of annotations and used it to implement a knowledge-based recommendation system on the CultureSampo portal. The method calculates the ontological relevance of RDF resources based on the  
Figure 1: A set of RDF triples extracted from DBpedia [9].  
number and type of data relationships encoded in an RDFS ontology, including class hierarchies and class-instance relations. Another example is the Art Recommender of the CHIP (Cultural Heritage Information Personalization) project on Rijksmuseum's InterAcrief database [21,22]. The system recommends art-related concepts based on user ratings of artwork and concepts from the underlying ontology used to model the museum's collections. Recommender systems empowered using semantic technologies have also been developed in other domains such as e-commerce [23], e-learning [24], and job recruitment [25].