print(deep_feature) This example simplifies many aspects and is intended to illustrate the process. Real-world applications might use more sophisticated models (like BERT for text embeddings) and incorporate additional metadata.
import numpy as np from gensim.models import Word2Vec Mission Impossible 4 Ghost Protocol Dual Audio 720p
# Training a simple Word2Vec model model = Word2Vec(sentences, vector_size=100, min_count=1) Mission Impossible 4 Ghost Protocol Dual Audio 720p
# Getting a vector for a word def get_word_vector(word): try: return model.wv[word] except KeyError: return np.zeros(100) # Default vector for out-of-vocabulary words Mission Impossible 4 Ghost Protocol Dual Audio 720p
# Concatenate all vectors for a deep feature deep_feature = np.concatenate([title_vector, genre_vector, resolution_vector, audio_vector, part_of_series_vector])