NCA-GENL Question 54
Select 4You are tasked with building a recommendation system that suggests similar products to users based on product descriptions. To accomplish this, you decide to use text embeddings generated by a pre-trained language model. Which of the following steps should you take to correctly integrate and use the embeddings for this task?
- A
Choose a pre-trained language model that supports embedding extraction, such as BERT or GPT.
- B
Input the product descriptions into the model to generate embeddings for each product.
- C
Apply clustering algorithms like k-means directly on the raw product descriptions to generate recommendations.
- D
Normalize the generated embeddings to ensure consistent vector magnitudes before using them in similarity comparisons.
- E
Use cosine similarity or other distance metrics to find similar products based on their embeddings.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To build a recommendation system using text embeddings, you need to first select a pre-trained language model capable of generating embeddings. Then, you process the product descriptions to create embeddings for each product. Normalizing the embeddings ensures consistent similarity comparisons, and using metrics like cosine similarity helps identify relevant products. Clustering raw text directly is not a viable approach, as it doesn't leverage the semantic power of embeddings.
- A. Correct.
This is correct because pre-trained language models like BERT and GPT can generate meaningful text embeddings that capture semantic relationships.
- B. Correct.
This is correct because generating embeddings from the input product descriptions is necessary to use the language model for this task.
- C. Incorrect.
This is incorrect because clustering algorithms like k-means require numerical data (e.g., embeddings), not raw text, and cannot directly process product descriptions.
- D. Correct.
This is correct because normalizing embeddings ensures that similarity metrics like cosine similarity yield more meaningful results by eliminating the influence of vector magnitude.
- E. Correct.
This is correct because cosine similarity is commonly used to compare embeddings and find similar items based on semantic relationships.