# Towards a Semantics-Based Recommendation System for Cultural Heritage Collections
## 4 Conclusions and Future Work  
Semantic technologies have been widely applied to the cultural sector to improve information management, addressing the challenges posed by the diversity and heterogeneity of cultural data. In most cases, the target users of the tools and applications that have been developed using such technologies are administrators, professionals, and researchers. Less attention has been paid so far to the needs of the end users. This paper proposes recommendation methods and describes the architecture of a recommender system for cultural heritage collections based on graph databases and semantic technologies, in an attempt to exploit the semantic description of cultural collections for the benefit of the end users. The proposed system aims to assist users who search and browse cultural collections in cultural semantic portals with finding objects related to particular themes based on their semantic annotations. Exploiting the similar graph-based structures of RDF datasets and graph databases, it uses a powerful graph database system, Neo4j, to manage the available semantic metadata and implement various recommendation algorithms that account for the structure of the available semantic metadata, the preferences of the users, and their viewing behaviours. We developed and tested the recommendation methods on top of the Archive of the Art Textbooks of Elementary and Public Schools in the Japanese Colonial Period (\"E \"E \"E \"E \"E \"E \"E \"E \"E \"E \"E \"E \"E \"E \"E \" \"E \"E \"), an RDF-based description of a cultural collection enriched with subject and keyword metadata. One limitation of our study is the quality of the data that we used for developing and testing the recommendation methods. The example dataset is well-formatted and contains various semantic metadata, which we used to implement the recommendation algorithms. However, some of these annotations are incomplete or inconsistent, thus potentially affecting the performance of the algorithms. For example, some of the subjects or keywords are provided in two languages (English and Chinese); this duplication of information can lead to problematic recommendations. We did not address this issue in the current study due to limited time but also because we wanted to keep the dataset intact. The next steps of this work are mostly concerned with the further development of the proposed recommendation system and its evaluation. First, we want to test the algorithms with more semantic datasets and develop variations of the algorithms that will be able to utilise other types of semantic metadata. In addition to producing better recommendations, we also want our system to be easily adjustable and configurable to other types and formats of metadata. Another important next step is to implement a full version of the system based on the architecture we describe in this paper. We have already implemented parts of the system, such as the underlying graph database and the recommendation algorithms. We also want to develop the system interface and deploy the system on an existing semantic cultural portal and test it in a realistic context. A final but equally crucial step is to evaluate the system with real users. As described in Section 3, we have verified the soundness of the methods we developed using the test dataset. After developing the first full version of the system, we will evaluate the performance of the algorithm with real users using the process and the measures we describe in Section 3.4. We will also evaluate other aspects of the system such as its functionality, usability, reliability, performance, and scalability. Although there is still a long way to go, we believe that the ideas and methods we present in this paper can demonstrate the potential of semantic and graph database technologies in exploiting existing semantic resources to provide intelligent end-user services in the cultural sector and inspire other digital humanities and data science researchers to further explore the potential of these technologies.