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- Title
Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives.
- Authors
Wang, Jiayun; Batjargal, Biligsaikhan; Maeda, Akira; Kawagoe, Kyoji; Akama, Ryo
- 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 Europeana dataset, we also demonstrate the versatility and scalability of our proposed model.
- Subjects
BOLTZMANN machine; DIGITAL libraries; RECOMMENDER systems; DIGITAL preservation; INFORMATION-seeking behavior; SCALABILITY; METADATA
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 4, p2435
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13042435