Abstract
The purpose of recommendation systems is to recommend users to the items which they are interested in. Recent studies have mentioned the individual models of recommendation systems, and each of such models uses different measures to predict users’ level of interest in items. In order to improve the efficiency of recommendation systems, we have built a hybrid model to combine such different measures. Then, we experimented the proposed model on the ML-20M-GroupLens dataset, and the obtained results have shown that our hybrid model based on weighted ratings improves up to 8.02 % in precision compared to the user-based collaborative filtering model (the individual model that gave the highest precision results on our experiments).
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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Ho, TL., Le, AC., Vu, DH. (2021). A Hybrid Model for Recommendation Systems. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_40
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DOI: https://doi.org/10.1007/978-981-33-6757-9_40
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