Explanation

Text Embeddings (Option C) is the correct answer because:

• Text embeddings convert text into dense numeric vectors that capture semantic meaning and relationships between words
• These vector representations preserve semantic similarity - similar items will have vectors that are close together in the embedding space
• This makes them ideal for recommendation systems where you want to find similar items based on their descriptions

Why the other options are incorrect:

• Tokenization (A): This is just the process of splitting text into individual tokens/words, but doesn't create numeric vectors
• Bag-of-Words (B): This creates sparse vectors based on word frequency, but doesn't capture semantic meaning or relationships between words
• Stemming (D): This is a text preprocessing technique that reduces words to their root form, but doesn't create numeric vector representations

Text embeddings (like Word2Vec, GloVe, or modern transformer-based embeddings) are specifically designed for tasks like similarity matching and recommendation systems where semantic understanding is crucial.