| G. Drakopoulos, I. Giannoukou, Ph. Mylonas, S. Sioutas |
| Self Organizing Maps For Cultural Content Delivery |
| Neural Computing and Applications, April 2022 |
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ABSTRACT
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| 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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| 29 April , 2022 |
| G. Drakopoulos, I. Giannoukou, Ph. Mylonas, S. Sioutas, "Self Organizing Maps For Cultural Content Delivery", Neural Computing and Applications, April 2022 |
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