Abstract
Collaborative filtering (CF), the most efficient technique in recommendation systems, can be classified into two types: neighborhood-based model and latent factor model. Both are only based on the user-item interaction, or rating information, and do not take into account the item’s content-based information which may contain valuable knowledge. In this work, we propose a hybrid content-based and neighborhood-based recommendation system which utilizes the genome tag associated with each movie in the MovieLens 20M dataset. Experiment results show that our proposed system not only achieves a comparable accuracy but also performs at least 2 times faster than the “pure” CF methods.
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This research is funded by Ministry of Science and Technology (MOST) under grant number 10/2018/ĐTCT-KC.01.14/16-20.
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Duong, T.N. et al. (2020). A Novel Hybrid Recommendation System Integrating Content-Based and Rating Information. In: Barolli, L., Nishino, H., Enokido, T., Takizawa, M. (eds) Advances in Networked-based Information Systems. NBiS - 2019 2019. Advances in Intelligent Systems and Computing, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-29029-0_30
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DOI: https://doi.org/10.1007/978-3-030-29029-0_30
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