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Self organizing maps for cultural content delivery

  • S.I. :Deep learning modelling in real life: (Anomaly Detection, Biomedical, Concept Analysis, Finance, Image analysis, Recommendation)
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Abstract

Tailored analytics play a key role in the successful delivery of cultural content to huge and diverse groups. Primarily the latter depends on a number of information retrieval factors determining user experience quality, most prominently precision, recall, and timing. These imply that cultural analytics should be designed with strong predictive power. In turn, the latter relies heavily on the clustering of the system user base. A self organizing map is a neural network architecture trained in an unsupervised way through a modified Hebbian rule to couple distances between two distinct spaces such that a manifold in the high dimensional space is projected smoothly to the lower dimensional one. The twofold focus of this work is the development of a tensor user distance metric for SOMs as well as the inclusion of behavioral attributes therein, both aiming at additional descriptive power and clustering flexibility. As a concrete example, the proposed SOMs are applied to data taken from a cultural content delivery system. The proposed methodology is evaluated based on a scoring method assessing both complexity and clustering quality criteria, including the number of epochs, the average cluster distance, and the topological error, with encouraging results.

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Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Competitiveness, Entrepreneurship and Innovation Operational Programme, under the Call “Research – Create – Innovate”, project title: “Development of technologies and methods for cultural inventory data interoperability”, project code: T1EDK-01728, MIS code: 5030954.

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Authors and Affiliations

  1. Department of Informatics, Ionian University, Plateia Tsirigoti 7, Kerkyra, 49100, Hellas

    Georgios Drakopoulos & Phivos Mylonas

  2. Department of Management Science and Technology, University of Patras, Panepistimio Patron, Patra, 26504, Hellas

    Ioanna Giannoukou

  3. Computer Engineering and Informatics Department, University of Patras, Panepistimio Patron, Patra, 26504, Hellas

    Spyros Sioutas

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  1. Georgios Drakopoulos
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Correspondence to Georgios Drakopoulos.

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Drakopoulos, G., Giannoukou, I., Mylonas, P. et al. Self organizing maps for cultural content delivery. Neural Comput & Applic 34, 19547–19564 (2022). https://doi.org/10.1007/s00521-022-07376-1

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