A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine…

Question

Options

• ALatent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the
• BA neural network with a minimum of three layers and random initial weights to identify patterns in
• CCollaborative filtering based on user interactions and correlations to identify patterns in the
• DRandom Cut Forest (RCF) over random subsamples to identify patterns in the customer

• A
  14% (6)
• B
  2% (1)
• C
  77% (34)
• D
  7% (3)

Explanation

Collaborative filtering is specifically designed to identify patterns in user behavior by leveraging interactions between users and items (products). It detects correlations - users who exhibit similar browsing/purchase patterns are likely to share preferences - making it ideal for recommendation engines. Option A (LDA) is a topic modeling technique for discovering abstract topics in document collections, not for behavioral pattern discovery in transactional data. Option B (neural network with random weights) is too generic and gives no direction for this recommendation use case. Option D (Random Cut Forest) is an unsupervised anomaly detection algorithm, not a recommendation or pattern-matching approach.

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