Abstract
Recommendation systems provide the facility to understand a person’s taste and find new, desirable content for them based on aggregation between their likes and rating of different items. In this paper, we propose a recommendation system that predict the note given by a user to an item. This recommendation system is mainly based on unsupervised topological learning. The proposed approach has been validated on MovieLens dataset and the obtained results have show very promising performances.
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The authors would like to acknowledge the support of FUI HERMES for providing financial support.
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Falih, I., Grozavu, N., Kanawati, R., Bennani, Y. (2015). A Recommendation System Based on Unsupervised Topological Learning. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_26
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DOI: https://doi.org/10.1007/978-3-319-26535-3_26
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