Sure! Here's an updated code that uses the Anime Recommendation Database 2020 from Kaggle and modifies the model architecture to fit the new data. Please note that this code assumes that you have downloaded and extracted the dataset, and that the extracted files are stored in the same directory as the script.
```python
import pandas as pd
import numpy as np
import keras
from keras.models import Model
from keras.layers import Input, Dense, Embedding, Conv2D, MaxPooling2D, concatenate, Flatten, Dropout
from sklearn.model_selection import train_test_split

# Load data
anime = pd.read_csv("anime.csv")
ratings = pd.read_csv("rating_complete.csv")

# Filter out users with less than 10 ratings and anime with less than 100 ratings
user_counts = ratings['user_id'].value_counts()
rating_counts = ratings['anime_id'].value_counts()
ratings = ratings[ratings['user_id'].isin(user_counts[user_counts >= 10].index)]
ratings = ratings[ratings['anime_id'].isin(rating_counts[rating_counts >= 100].index)]

# Merge anime and rating data
data = pd.merge(anime, ratings, on='anime_id')

# Filter out unwanted columns
data = data[['user_id', 'anime_id', 'rating', 'genre', 'type', 'episodes', 'members']]

# Split data into train and test sets
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

# Get number of users and items
num_users = len(data['user_id'].unique())
num_anime = len(data['anime_id'].unique())

# Preprocess text data
MAX_WORDS = 10000
MAX_SEQUENCE_LENGTH = 100
tokenizer = keras.preprocessing.text.Tokenizer(num_words=MAX_WORDS, lower=True, split=' ')
tokenizer.fit_on_texts(data['genre'])
train_text = tokenizer.texts_to_sequences(train_data['genre'])
test_text = tokenizer.texts_to_sequences(test_data['genre'])
train_text = keras.preprocessing.sequence.pad_sequences(train_text, maxlen=MAX_SEQUENCE_LENGTH)
test_text = keras.preprocessing.sequence.pad_sequences(test_text, maxlen=MAX_SEQUENCE_LENGTH)

# Preprocess image data
def preprocess_image(filename):
    img = keras.preprocessing.image.load_img(filename, target_size=(64, 64))
    img_array = keras.preprocessing.image.img_to_array(img)
    return img_array / 255.

train_image = np.array([preprocess_image(f"images/{id}.jpg") for id in train_data['anime_id']])
test_image = np.array([preprocess_image(f"images/{id}.jpg") for id in test_data['anime_id']])

# Preprocess numerical data
train_numerical = train_data[['members', 'episodes']].values
test_numerical = test_data[['members', 'episodes']].values

# Preprocess categorical data
train_categorical = pd.get_dummies(train_data['type'])
test_categorical = pd.get_dummies(test_data['type'])

# Define model architecture
user_input = Input(shape=(1,), name='user_input')
anime_input = Input(shape=(1,), name='anime_input')
text_input = Input(shape=(MAX_SEQUENCE_LENGTH,), name='text_input')
embedded_text = Embedding(input_dim=MAX_WORDS, output_dim=100, input_length=MAX_SEQUENCE_LENGTH)(text_input)
text_flattened = Flatten()(embedded_text)
image_input = Input(shape=(64, 64, 3), name='image_input')
conv1 = Conv2D(32, (3, 3), activation='relu')(image_input)
pool1 = MaxPooling2D((2, 2))(conv1)
conv2 = Conv2D(64, (3,
```
다음은 Kaggle의 애니메이션 추천 데이터베이스 2020을 사용하고 새 데이터에 맞게 모델 아키텍처를 수정하는 업데이트된 코드입니다. 이 코드는 데이터 집합을 다운로드하여 추출했으며 추출된 파일이 스크립트와 동일한 디렉터리에 저장되어 있다고 가정한다는 점에 유의하세요.
```python
임포트 팬더로 PD
import numpy as np
import keras
keras.models에서 모델 가져오기
keras.layers에서 입력, 밀도, 임베딩, 컨브2D, 맥스풀링2D, 컨케트네이트, 플랫텐, 드롭아웃을 가져옵니다.
sklearn.model_selection에서 train_test_split 가져오기

# 데이터 로드
anime = pd.read_csv("anime.csv")
ratings = pd.read_csv("rating_complete.csv")

# 평점 10점 미만의 사용자와 평점 100점 미만의 애니메이션을 필터링합니다.
user_counts = ratings['user_id'].value_counts()
rating_counts = ratings['anime_id'].value_counts()
ratings = ratings[ratings['user_id'].isin(user_counts[user_counts >= 10].index)]
ratings = ratings[ratings['anime_id'].isin(rating_counts[rating_counts >= 100].index)]

# 애니메이션과 평점 데이터 병합
data = pd.merge(anime, ratings, on='anime_id')

# 원하지 않는 열 필터링
data = data[['user_id', 'anime_id', 'rating', 'genre', 'type', 'episodes', 'members']]]

# 데이터를 훈련 세트와 테스트 세트로 분할
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

# 사용자 수와 아이템 수 가져오기
num_users = len(data['user_id'].unique())
num_anime = len(data['anime_id'].unique())

# 텍스트 데이터 전처리
max_words = 10000
max_sequence_length = 100
tokenizer = keras.preprocessing.text.Tokenizer(num_words=MAX_WORDS, lower=True, split=' ')
tokenizer.fit_on_texts(data['genre'])
train_text = tokenizer.texts_to_sequences(train_data['genre'])
test_text = tokenizer.texts_to_sequences(test_data['genre'])
train_text = keras.preprocessing.sequence.pad_sequences(train_text, maxlen=MAX_SEQUENCE_LENGTH)
test_text = keras.preprocessing.sequence.pad_sequences(test_text, maxlen=MAX_SEQUENCE_LENGTH)

# 이미지 데이터 사전 처리
def preprocess_image(filename):
    img = keras.preprocessing.image.load_img(filename, target_size=(64, 64))
    img_array = keras.preprocessing.image.img_to_array(img)
    반환 img_array / 255.

train_image = np.array([preprocess_image(f"images/{id}.jpg") for id in train_data['anime_id']])
test_image = np.array([preprocess_image(f"images/{id}.jpg") for id in test_data['anime_id']])

# 숫자 데이터 전처리
train_numerical = train_data[['members', 'episodes']].values
test_numerical = test_data[['members', 'episodes']].values

# 범주형 데이터 전처리
train_categorical = pd.get_dummies(train_data['type'])
test_categorical = pd.get_dummies(test_data['type'])

# 모델 아키텍처 정의
user_input = Input(모양=(1,), 이름='user_input')
anime_input = Input(shape=(1,), name='anime_input')
text_input = 입력(모양=(MAX_SEQUENCE_LENGTH,), 이름='text_input')
embedded_text = 임베딩(입력_dim=MAX_WORDS, 출력_dim=100, 입력_길이=MAX_SEQUENCE_LENGTH)(text_input)
text_flattened = Flatten()(embedded_text)
image_input = Input(shape=(64, 64, 3), name='image_input')
conv1 = Conv2D(32, (3, 3), 활성화='relu')(image_input)
pool1 = MaxPooling2D((2, 2))(conv1)
conv2 = Conv2D(64, (3,
```