DEA-C01 Question 137
Single answerYou are building a recommendation system for an e-commerce platform on AWS. The system needs to identify relationships between products frequently bought together by analyzing transactional data. Which data structure and algorithm combination is the most appropriate for efficiently solving this problem?
- A
Graph data structure with Breadth-First Search (BFS)
- B
Graph data structure with Depth-First Search (DFS)
- C
Graph data structure with PageRank algorithm
- D
Graph data structure with Minimum Spanning Tree algorithm
Show answer and explanation
Correct answer: C
Explanation
For building a recommendation system that identifies relationships between products frequently bought together, a graph data structure is appropriate because it can model the relationships between products as nodes and edges. Among the algorithms listed, PageRank is the most suitable because it ranks items based on their connections, making it ideal for identifying and prioritizing relationships in a product recommendation scenario.
- A. Incorrect.
Breadth-First Search (BFS) is used for traversing or searching tree or graph data structures. While it is useful for finding the shortest path or connected components, it is not designed for identifying relationships like product co-purchases.
- B. Incorrect.
Depth-First Search (DFS) is another traversal algorithm for graphs or trees. Similar to BFS, it is not specifically suited for analyzing weighted relationships or ranking relationships between products.
- C. Correct.
The PageRank algorithm, when applied to a graph data structure, is highly suitable for identifying relationships and ranking items based on their connections. This makes it a good choice for finding and ranking frequently bought-together products in a recommendation system.
- D. Incorrect.
The Minimum Spanning Tree algorithm is used to find the subset of edges in a weighted graph that connects all vertices with the smallest total weight. It is not designed for analyzing relationships or ranking items in a recommendation system.