Abstract
Weighted tree representation and its similarities are widely used in e-learning and e-business where items are represented using ontologies and user preferences are given by weights. However, there have been some challenges to develop recommendation systems where items are represented using weighted trees. Recommendation systems consist of content-based where similarity between items plays a major role, collaborative filtering where user rating plays a major role and hybrid approaches. This paper proposes a new hybrid recommendation system which uses generalized-weighted tree similarity to find similarity between items that also includes user rating to integrate with collaborative filtering. This new hybrid recommendation system has flexibility to include preferences for different aspects of items using weighted trees and user ratings as well. This paper addresses the challenge of using recursive weighted tree similarity in hybrid recommendation system. We established theoretical and experimental evaluation among a few example trees using our proposed recommendation systems.
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Krishna, D.P., Venu Gopal Rao, K. (2018). Recommendation System Based on Generalized-Weighted Tree Similarity Algorithm. In: Dash, S., Naidu, P., Bayindir, R., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-7868-2_15
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DOI: https://doi.org/10.1007/978-981-10-7868-2_15
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