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Q2. An e-commerce platform wants to build a product recommendation system that finds similar items based on text descriptions. They plan to convert text into numeric vectors first. Which technique should they use?
Real Exam
Community
JJin
A
Tokenization
B
Bag-of-Words
C
Text Embeddings
D
Stemming
Explanation:
Explanation
Text Embeddings is the correct answer because:
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Purpose: Text embeddings convert text into dense numeric vectors (embeddings) that capture semantic meaning and relationships between words and phrases.
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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.
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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.
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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.
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AWS context: AWS offers services like Amazon SageMaker with built-in algorithms for text embeddings, and Amazon Kendra for semantic search capabilities.
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