AWS Certified Cloud Practitioner

Explanation

Cosine Similarity is the correct algorithm for this similarity search task because:

• Vector Representation: The problem states that both the query and research papers are converted into vectors, which is a common approach in natural language processing and information retrieval.

• Similarity Measurement: Cosine similarity measures the cosine of the angle between two vectors in a multi-dimensional space, making it ideal for comparing document vectors regardless of their magnitude.

• Document Similarity: This algorithm is widely used in text analysis, recommendation systems, and information retrieval to find similar documents based on their vector representations.

Why other options are incorrect:

• Decision Trees (A): Used for classification and regression tasks, not for vector similarity search.
• Random Forest (C): An ensemble learning method for classification and regression, not designed for similarity matching between vectors.
• Token Merging (D): This is not a standard algorithm for similarity search; it might refer to token processing techniques but doesn't measure similarity between vectors.

Cosine similarity is particularly effective for text similarity because it focuses on the orientation (angle) between vectors rather than their magnitude, making it robust to document length variations.