Answer: Text Embeddings

## 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.

Author: Ritesh Yadav