Answer: Text Embeddings

## Explanation **Text Embeddings** is the correct answer because: 1. **Purpose**: Text embeddings convert text into dense numeric vectors (embeddings) that capture semantic meaning and relationships between words and phrases. 2. **Similarity Search**: Embeddings create vector representations where similar items have vectors that are close together in the vector space, making them ideal for finding similar products based on text descriptions. 3. **Comparison with other options**: - **Tokenization (A)**: This is the process of breaking text into individual tokens (words, subwords) but doesn't create numeric vectors. - **Bag-of-Words (B)**: Creates sparse vectors based on word frequency but loses word order and semantic relationships, making it less effective for similarity search. - **Stemming (D)**: Reduces words to their root form but doesn't create numeric representations. 4. **Real-world application**: For e-commerce product recommendations, text embeddings (like Word2Vec, GloVe, or BERT embeddings) can capture that "laptop" and "notebook computer" are similar, even if they don't share exact words. 5. **AWS context**: AWS offers services like Amazon SageMaker with built-in algorithms for text embeddings, and Amazon Kendra for semantic search capabilities.

Author: Jin H