Predicting User Interests with CatBoost Model

This function uses the CatBoost library to predict user interests. It takes a user ID and a feature array as inputs, then uses a preloaded model to make predictions and returns the results.

Technology Stack : CatBoost, NumPy

Code Type : Function

Code Difficulty : Intermediate

                
                    
def predict_user_interest(user_id, features):
    import catboost as cb
    import numpy as np

    # 假设已经加载了训练好的模型
    model = cb.CatBoostModel.load('user_interest_model.cb')

    # 假设features是一个numpy数组或者可以转换为numpy数组的结构
    features = np.array(features)

    # 使用模型进行预测
    predictions = model.predict(features)

    # 返回预测结果
    return predictions