我没有用户数据,所以只是基于招聘信息来写,下面你给初完成的代码
时间: 2024-03-24 07:35:57 浏览: 14
好的,下面是一个简单的基于招聘信息的推荐系统的代码,使用了XDeepFM模型:
```python
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from deepctr.models import xDeepFM
from deepctr.inputs import SparseFeat, DenseFeat, get_feature_names
# 读取数据
data = pd.read_csv('job_data.csv')
# 数据预处理
data.drop_duplicates(inplace=True) # 去重
data.fillna(0, inplace=True) # 缺失值填充
# 特征处理
sparse_features = ['location', 'job_information', 'job_location', 'experiments', 'education', 'company_name', 'co_experiments', 'company_type', 'company_size']
dense_features = ['avg_high_salary', 'avg_low_salary']
target = ['avg_high_salary']
# 标签编码
for feat in sparse_features:
lbe = LabelEncoder()
data[feat] = lbe.fit_transform(data[feat])
# 归一化
mms = MinMaxScaler()
data[dense_features] = mms.fit_transform(data[dense_features])
# 构建输入
fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(), embedding_dim=4)
for i, feat in enumerate(sparse_features)] + [DenseFeat(feat, 1, )
for feat in dense_features]
dnn_feature_columns = fixlen_feature_columns
linear_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)
train, test = train_test_split(data, test_size=0.2)
train_model_input = {name: train[name].values for name in feature_names}
test_model_input = {name: test[name].values for name in feature_names}
# 模型训练
model = xDeepFM(linear_feature_columns, dnn_feature_columns, task='regression')
model.compile("adam", "mse", metrics=['mse'],)
history = model.fit(train_model_input, train[target].values,
batch_size=256, epochs=10, verbose=2, validation_split=0.2, )
# 模型预测
pred = model.predict(test_model_input, batch_size=256)
```
需要注意的是,由于缺少用户数据,这里只是简单地使用了职位信息作为输入,实际应用中需要根据用户的求职意向、工作经验、学历等信息来构建用户-物品矩阵,从而更加准确地推荐职位。另外,模型的性能也需要根据实际情况进行评估和优化。