Abstract
With the rapid development of the Internet, leading to the problem of information overload, how to find the satisfied demand from the overloaded information becomes an urgent problem to be solved, which leads to the emergence of a recommendation system. In order to solve the problem of the lack of sex of the user and the imbalance of existing samples in the recommendation system, this paper proposes to combine the smote with the random forest to forecast the gender. Compared with other models, the experimental results show the effectiveness of the proposed method by conducting experiments on real e-commerce platform data.
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At the completion of this essay, I would like to thank all those who provided guidance on this essay. At the same time thanks experts and staff for reviewing my dissertation.
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Meigen, H., Wenhao, C. (2019). One of the Smote_rf’s Gender Prediction Methods in Recommendation System. In: Xhafa, F., Patnaik, S., Tavana, M. (eds) Advances in Intelligent, Interactive Systems and Applications. IISA 2018. Advances in Intelligent Systems and Computing, vol 885. Springer, Cham. https://doi.org/10.1007/978-3-030-02804-6_47
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DOI: https://doi.org/10.1007/978-3-030-02804-6_47
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