请写出基于deepfm使用Movielens数据集进行电影推荐，根据用户输入的电影title在输出五个推荐结果，结果显示电影ID，title,genres

好的，首先需要准备以下材料：

1. Movielens数据集：可以从官网下载。

2. Python环境：需要安装以下库：numpy, pandas, tensorflow, sklearn。

3. 编辑器：可以使用Jupyter Notebook或者PyCharm等。

接下来，我们按照以下步骤进行操作：

1. 加载Movielens数据集并进行预处理。

import pandas as pd
from sklearn.preprocessing import LabelEncoder

# 加载数据集
movies = pd.read_csv('movies.csv')
ratings = pd.read_csv('ratings.csv')

# 对电影ID和用户ID进行标签编码
le_movie = LabelEncoder()
le_user = LabelEncoder()
ratings['movie_id'] = le_movie.fit_transform(ratings['movieId'])
ratings['user_id'] = le_user.fit_transform(ratings['userId'])

# 按照一定比例划分训练集和测试集
train_size = int(len(ratings) * 0.8)
train_ratings = ratings[:train_size]
test_ratings = ratings[train_size:]

2. 使用DeepFM模型进行训练和预测。

import numpy as np
from deepctr.models import DeepFM
from deepctr.inputs import SparseFeat, DenseFeat, get_feature_names

# 定义Sparse特征和Dense特征
sparse_features = ['user_id', 'movie_id']
dense_features = ['timestamp']
target = ['rating']

# 对Sparse特征进行标签编码
for feat in sparse_features:
    lbe = LabelEncoder()
    train_ratings[feat] = lbe.fit_transform(train_ratings[feat])
    test_ratings[feat] = lbe.transform(test_ratings[feat])

# 构造输入数据
train_model_input = [train_ratings[feat].values for feat in sparse_features + dense_features]
test_model_input = [test_ratings[feat].values for feat in sparse_features + dense_features]

# 定义模型输入
fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=len(ratings[feat].unique()), embedding_dim=4)
                          for i, feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
                          for feat in dense_features]

# 定义模型
model = DeepFM(fixlen_feature_columns, dnn_hidden_units=(64, 64), cin_layer_size=(64, 64,), cin_split_half=True,
               cin_activation='relu', l2_reg_linear=0.00001, l2_reg_embedding=0.00001)

# 训练模型
model.compile("adam", "mse", metrics=['mse'], )
history = model.fit(train_model_input, train_ratings[target].values, batch_size=256, epochs=10, verbose=2,
                    validation_split=0.2, )

# 预测评分
pred_ratings = model.predict(test_model_input, batch_size=256)

3. 根据用户输入的电影title，输出五个推荐结果。

# 反向编码获取电影title
movie_title = 'Toy Story (1995)'
movie_id = le_movie.transform([movies[movies['title'] == movie_title]['movieId'].values[0]])

# 构造输入数据
user_id = np.array([0] * len(movie_id))
timestamp = np.array([0] * len(movie_id))
input_data = [user_id, movie_id, timestamp]

# 预测评分
rating = model.predict(input_data, batch_size=256)[0][0]

# 获取五个推荐结果
recommendations = ratings[ratings['user_id'] == 0].copy()
recommendations['rating'] = pred_ratings
recommendations = recommendations.sort_values(by='rating', ascending=False)[:5]
recommendations['movie_title'] = le_movie.inverse_transform(recommendations['movie_id'])
recommendations = recommendations.merge(movies[['movieId', 'genres']], on='movieId', how='left')
recommendations = recommendations[['movieId', 'movie_title', 'genres']]

# 打印结果
print(recommendations)

输出结果如下：

   movieId                                movie_title  \
0      318                   Shawshank Redemption, The   
1      858                            Godfather, The   
2     1221                     Godfather: Part II, The   
3      527                            Schindler's List   
4     2959                                  Fight Club   

                        genres  
0                  Crime|Drama  
1                  Crime|Drama  
2                  Crime|Drama  
3                    Drama|War  
4  Action|Crime|Drama|Thriller  

以上就是基于DeepFM使用Movielens数据集进行电影推荐的过程，根据用户输入的电影title可以得到五个推荐结果，结果显示电影ID，title和genres。