# Towards a Semantics-Based Recommendation System for Cultural Heritage Collections
## 3 Developing a Recommendation System for Cultural Heritage Collections
### Identifying the Users' Interests  
The system we developed targets users who have specific search goals but cannot pinpoint certain objects, which is common among cultural heritage experts [38] and could also happen to common users. For example, a curator might seek objects of specific themes in large collections they are not familiar with or a researcher might look for objects with features similar to those they already found in a collection [38]. While some cultural portals provide keyword searching and feature filters, these functions cannot meet the needs of every user, especially when they search for objects related to very specific topics that are not covered by the filters or when users have little knowledge of the terms used to describe the cultural heritage objects in the metadata. The system identifies users' interests in two ways. At the beginning of a search, we assume that users will input information they think is relevant to their search goal, which \begin{table}
\begin{tabular}{c c} \hline \hline
**Relationship Type** & **Amount** \\ \hline type & 5091 \\ author & 423 \\ editor & 21 \\ printer & 395 \\ subject & 1167 \\ keywords & 1434 \\ isShownAt & 1697 \\ isShownBy & 1697 \\ CollectedBy & 1721 \\ Distributor & 306 \\ aggregatedCHO & 1697 \\ aat2427\_produced\_by & 1326 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship types and numbers in the graph database. \begin{table}
\begin{tabular}{l c l} \hline \hline
**Node Type (Label)** & **Amount** & **Associated Node Attribute** \\ \hline AscdcAggregation & 1697 & uri \\  & & title, uri, subject, rights, relation, printer, label, keywords, identifier, historyNote, editor, distributor, description, datePublished, dateCreated, dataset, collectedBy, author \\ AscdcWebResource & 1697 & uri, license \\ Resource & 7340 & uri \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Node labels and attributes in the graph database. serves as the initial profile of the search. We assume that the searching interface provides subject and keyword lists and that the user can select and/or exclude items from the lists. When the system receives input information, it adds weight to the corresponding metadata so that the objects that match or are close to users' interests are prioritised over other objects. Users' interests are also inferred from the objects they have viewed. Users usually browse several objects before they find the ones they are most interested in. The viewed object, if not the perfect match, is probably similar to what the users are looking for in some ways. Every time the user views an object, the system considers the object as part of the user's interests and adds weight to the subjects and keywords it relates to, unless the user marks the object as irrelevant, which we assume the viewing interface allows users to do. The change in weights will affect the recommendations the system provides for users at later stages. This approach aims to identify object features that users may be interested in but are not able to indicate using the subject and keyword lists provided by the system. In the AS-NTUE-School-Art-Textbooks dataset, the system produces recommendations based on two metadata types: subject and keywords. The two metadata types are utilised mainly because they manifest the advantage of semantic data retrieval over text-based approaches. If a user looks for artefacts created by a certain artist, searching for the name of the artist with text could be as effective as searching for the semantic metadata. But a text-based programme can hardly process non-textual information, such as contents of images and 3D models, which requires additional annotations, such as subject and keyword metadata. Furthermore, as different terms (synonyms) may be used to describe the same feature of cultural objects, users may fail in finding the object they want only because they do not search for the specific descriptive term used in the dataset.