Abstract
Product recommendation based on user behavior is a hot research topic In the Internet era in the same data set, the features that the results of the various classifications are a greater difference were handled with random forest model. This paper compares the mainstream classification algorithm C4.5 and CART and analyzes 578,906,480 user behavior records on the results of actual transaction in Alibaba. The results show that CART decision tree algorithm is more suitable for large e-commerce data mining.
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Jiang, Y., He, L., Gao, Y., Wang, K., Hu, C. (2017). Comparison with Recommendation Algorithm Based on Random Forest Model. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_72
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DOI: https://doi.org/10.1007/978-981-10-3023-9_72
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