# Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives
###### Abstract  
Digital archives (DAs) usually store diverse expert-level materials. Nowadays, access to DAs is increasing for non-expert users, However, they might have difficulties formulating appropriate search queries to find the necessary information. In response to this problem, we propose a query log-based query recommendation algorithm that provides expert knowledge to non-expert users, thus supporting their information seeking in DAs. The use case considered is one where after users enter some general queries, they will be recommended semantically similar expert-level queries in the query logs. The proposed modified conditional restricted Boltzmann machines (M-CRBMs) are capable of utilizing the rich metadata in DAs, thereby alleviating the sparsity problem that conventional restricted Boltzmann machines (RBMs) will face. Additionally, compared with other CRBM models, we drop a large number of model weights. In the experiments, the M-CRBMs outperform the conventional RBMs when using appropriate metadata, and we find that the recommendation results are relevant to the metadata fields that are used in M-CRBMs. Through experiments on the European dataset, we also demonstrate the versatility and scalability of our proposed model. query recommendation; digital archives; conditional restricted Boltzmann machines; machine learning +
Footnote †: journal:  
The main purpose of our proposed method is to use the queries formulated by high-level experts to help other users find the appropriate query (or queries) to narrow down the scope of the search results. The proposed model can be downloaded on GitHub ([[https://github.com/blueorris/M-CRBMs](https://github.com/blueorris/M-CRBMs)]([https://github.com/blueorris/M-CRBMs](https://github.com/blueorris/M-CRBMs)), accessed on 22 December 2022). According to the work in [9], researchers are accessing Europeana ([[https://www.europeana.eu/portal/en](https://www.europeana.eu/portal/en)]([https://www.europeana.eu/portal/en](https://www.europeana.eu/portal/en)), accessed on 22 December 2022) [10], which is a web portal created by the European Union containing digitized museum collections of more than 3000 institutions across Europe for seeking materials, which is often considered the main purpose of the users coming to DAs. There are various types of users and various purposes for the users of Europeana. It is worth noting that the fact that various types of users (including cultural heritage enthusiasts, students, academics, teachers, cultural heritage professionals, and others) are accessing Europeana illustrates that users have different levels of understanding of the materials stored in DAs. The fact that the proportion of the purposes \"create new work\" and \"personal interest\" is larger than that of \"professional activities\" illustrates that seeking research-relevant materials is only part of the information needs in DAs. Therefore, it is necessary to rethink how to build the information access functions in DAs for supporting better usage. Since users who do not have a high level of understanding of the materials may fail to formulate appropriate queries for their purposes, we propose a method that recommends candidate queries to assist these users. Query recommendation (or query suggestion) assists users to refine queries in order to satisfy their information needs. Often, users try different queries until they are satisfied with the results. This will be hindered by having little knowledge about the information for which they are searching. As mentioned before, the users might vary from different levels of expert knowledge of the contents in DAs, thus causing difficulties in formulating appropriate queries for information seeking. For example, such difficulties occur when a user searches ukiyo-e (ukiyo-e is a kind of famous Japanese art of woodblock prints) databases. Users with basic background knowledge of ukiyo-e would formulate a simple search query, such as \"???