Abstract
Digital transformations led to the development of supportive technologies, new tools for smart education, and emergent branches of research in the domain of digital library services. This paper introduces a content-based recommender system for Romanian books. The reference documents are old and were digitized via Optical Character Recognition (OCR), a process that generated noise in the conversion. The current prototype version of our system is trained on a corpus of 50 OCRed books which are split into corresponding paragraphs; thus, recommendations of related books to the user’s input query are provided only with regards to these reference documents. The trained neural models consider a bidirectional RNN layer with LSTM or GRU cells over pre-trained Romanian FastText embeddings, followed by a global max-pooling layer. The study shows competitive results on predicting books given an input text, as the proposed model achieves an overall accuracy of around 90%.
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Acknowledgements
This work was funded by “Semantic Media Analytics—SEMANTIC,” subsidiary contract no. 20176/30.10.2019, from the NETIO project ID: P_40_270, MySMIS Code: 105976, as well as a grant of the Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689 “Revitalizing Libraries and Cultural Heritage through Advanced Technologies,” within PNCDI III.
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Nitu, M., Ruseti, S., Dascalu, M., Tomescu, S. (2021). Semantic Recommendations of Books Using Recurrent Neural Networks. In: Mealha, Ó., Rehm, M., Rebedea, T. (eds) Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Smart Innovation, Systems and Technologies, vol 197. Springer, Singapore. https://doi.org/10.1007/978-981-15-7383-5_20
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DOI: https://doi.org/10.1007/978-981-15-7383-5_20
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