请写出基于deepfm使用Movielens中ml-1m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

以下是基于DeepFM模型使用Movielens中ml-1m数据集进行电影推荐的示例代码：

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, MinMaxScaler
from deepctr.models import DeepFM
from deepctr.inputs import SparseFeat, DenseFeat, get_feature_names

# 加载数据集
data = pd.read_csv('ml-1m/ratings.dat', header=None, sep='::', engine='python', names=['user_id', 'movie_id', 'rating', 'timestamp'])
movies = pd.read_csv('ml-1m/movies.dat', header=None, sep='::', engine='python', names=['movie_id', 'title', 'genres'])

# 将电影信息与评分信息合并
data = pd.merge(data, movies, on='movie_id')

# 对特征进行编码
sparse_features = ['user_id', 'movie_id', 'title', 'genres']
for feat in sparse_features:
    lbe = LabelEncoder()
    data[feat] = lbe.fit_transform(data[feat])

# 对特征进行归一化处理
dense_features = ['timestamp']
mms = MinMaxScaler(feature_range=(0, 1))
data[dense_features] = mms.fit_transform(data[dense_features])

# 定义DeepFM模型的输入特征
fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(), embedding_dim=4)
                          for feat in 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)

# 构造训练集和测试集
train_data = data.sample(frac=0.8, random_state=2021)
test_data = data.drop(train_data.index)

train_model_input = {name: train_data[name] for name in feature_names}
test_model_input = {name: test_data[name] for name in feature_names}

# 训练DeepFM模型
model = DeepFM(linear_feature_columns, dnn_feature_columns, task='regression')
model.compile("adam", "mse", metrics=['mse'], )
model.fit(train_model_input, train_data['rating'].values, batch_size=256, epochs=10, verbose=2, validation_split=0.2, )

# 预测用户对电影的评分，并根据评分排序输出推荐结果
def recommend_movies(title):
    # 获取电影的ID
    movie_id = data[data['title'] == title]['movie_id'].values[0]
    # 构造用户输入
    user_id = np.array([0])
    movie_id = np.array([movie_id])
    title = np.array([data[data['movie_id'] == movie_id]['title'].values[0]])
    genres = np.array([data[data['movie_id'] == movie_id]['genres'].values[0]])
    timestamp = np.array([0])
    # 对特征进行编码和归一化处理
    user_id = lbe.transform(user_id)
    movie_id = lbe.transform(movie_id)
    title = lbe.transform(title)
    genres = lbe.transform(genres)
    timestamp = mms.transform(timestamp.reshape(-1, 1)).reshape(-1)
    # 预测用户对电影的评分
    pred = model.predict([user_id, movie_id, title, genres, timestamp])
    # 根据评分排序输出推荐结果
    topk = 5
    item_score_list = []
    for i in range(data['movie_id'].nunique()):
        item_id = np.array([i])
        title = np.array([data[data['movie_id'] == item_id]['title'].values[0]])
        genres = np.array([data[data['movie_id'] == item_id]['genres'].values[0]])
        item_id = lbe.transform(item_id)
        title = lbe.transform(title)
        genres = lbe.transform(genres)
        timestamp = np.array([0])
        timestamp = mms.transform(timestamp.reshape(-1, 1)).reshape(-1)
        score = model.predict([user_id, item_id, title, genres, timestamp])
        item_score_list.append((i, data[data['movie_id'] == item_id]['title'].values[0], data[data['movie_id'] == item_id]['genres'].values[0], score))
    item_score_list = sorted(item_score_list, key=lambda x: x[3], reverse=True)
    for i in range(topk):
        print('Movie ID:', item_score_list[i][0])
        print('Title:', item_score_list[i][1])
        print('Genres:', item_score_list[i][2])

用户可以调用 recommend_movies 函数，输入电影的 title，即可获得五个推荐结果。