# Towards a Semantics-Based Recommendation System for Cultural Heritage Collections
## 1 Introduction  
Information management in the field of cultural heritage has been challenged by the diversity and heterogeneity in cultural data. Cultural materials are massive in quantity, diverse in topic, intricate in interconnection, and varying in form when stored digitally [1]. Semantic technologies, including the RDF data model and RDF-compatible ontology languages such as OWL, have been widely used to address these challenges, enabling the organisation and interoperability of distributed cultural heritage databases based on the meaning of data. Using machine-readable metadata, semantic technologies enable not only richer semantic descriptions of cultural heritage entities but also their connection with semantic relations, such as material type, geographical origin, the creator or current owner of an artefact, etc.  
Several large cultural organisations have applied semantic technologies to their collections, including the British Museum, the Smithsonian American Art Museum, the Victoria and Albert Museum, Rijksmuseum, and many others, and several digital libraries that aggregate data from different collections have been developed using the same technologies, with European [2] being the most notable example. In most of these cases, the focus has been on improving the organisation and management of cultural data, with cultural heritage professionals and researchers being the main beneficiaries, while little attention has been paid to services and applications for the end users. For example, visitors to most museum websites still have to use keyword-based search engines to explore and search through their collections, and they often have to go through the overwhelming search results and comprehend the culturally entangled information before finding the objects they are looking for. The existing semantically annotated cultural datasets have significant potential in supporting more sophisticated search engines and other user-end intelligent services, such as association discovery, personalisation, and recommendation [1]; however, such types of services can rarely be found in cultural portals.  
Aiming to address this gap, this study develops recommendation methods making use of cultural heritage semantic datasets and the architecture of a recommender system for cultural heritage collections. The proposed methods aim to alleviate the users' burden when finding objects of interest in large collections by recommending objects based on their preferences and viewing behaviours. The methods were developed on top of a specific dataset, but they can easily be adapted to any semantic cultural dataset and reused by any cultural organisation that aims to improve the experience of visitors to their online collections. We implemented the proposed methods on top of the graph database platform Neo4j and its associated graph algorithms. By developing these recommendation methods and a recommender system architecture, we seek to achieve two main objectives:  
* Demonstrate ways that graph database technology can leverage the semantically rich cultural heritage collection datasets that are currently available in several cultural heritage institutions and online digital libraries.
* Develop the foundation for a semantics-based recommendation system aimed at facilitating the exploration of cultural heritage collections in a personalised way, taking into account the user's individual preferences as explicitly stated by the users or derived from their viewing behaviours.  
In the rest of this paper, we first present the background and related work of the study in Section 2. In Section 3, we present the recommendation methods and their implementation in a graph database; the dataset that we developed and used to test the methods; the architecture of the search and recommendation system for cultural heritage collections that uses the proposed methods; and their prospective evaluation. In the last section, we summarise and discuss the next steps of this work.