Answer: Text Embeddings

## Explanation **Text Embeddings** is the correct technique for converting text into numeric vectors for similarity-based recommendation systems. ### Why Text Embeddings? 1. **Semantic Understanding**: Text embeddings capture semantic meaning and contextual relationships between words, unlike simple tokenization or bag-of-words approaches. 2. **Dense Vector Representation**: They create dense, low-dimensional vector representations that preserve semantic similarity - similar items will have vectors that are close together in the vector space. 3. **Ideal for Similarity Search**: This makes embeddings perfect for recommendation systems where you need to find similar items based on text descriptions. ### Why Not the Other Options? - **Tokenization (A)**: This is just the process of breaking text into individual words or tokens - it doesn't create numeric vectors. - **Bag-of-Words (B)**: While this creates numeric vectors, it results in sparse, high-dimensional vectors that don't capture semantic meaning or word relationships well. - **Stemming (D)**: This is a text normalization technique that reduces words to their root form, but doesn't create numeric vectors. ### Real-World Application For an e-commerce recommendation system, text embeddings would allow the platform to: - Find products with similar descriptions even when different words are used - Capture nuanced relationships between products - Enable efficient similarity searches using vector databases This approach is commonly used in modern recommendation systems and natural language processing applications.

Author: Ritesh Yadav