Cellphones are electronic devices that people use most in daily life. There are various kinds of cellphones in the market nowadays, while ordinary consumers are dazzled by the huge cellphone market and do not know how to choose a suitable one. Hence, it is of great importance to help them in doing this kind of selection to satisfy their needs. This paper proposes a machine learning based cellphones recommendation approach, CIMR, for consumers to pick up the most suitable cellphones that can mostly meet their desired requirements. We evaluate CIMR by using thousands of real consumers all over the world with two proposed methods: Gradient Boosting Decision Tree (GBDT) and Random Forest (RF). The experimental results show that the accuracy rate for recommending cellphones by using our method is 95.87%, which exceeds the existing old methods by up to 5.49%.
|Name||Proceedings - 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019|
|Conference||International Conference on Artificial Intelligence and Advanced Manufacturing (1st : 2019)|
|Abbreviated title||AIAM 2019|
|Period||17/10/19 → 19/10/19|
- Machine learning
- Cellphone Recommendation
- Data Mining