Which solution should the Specialist recommend?
Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
Explanations:
Latent Dirichlet Allocation (LDA) is primarily used for topic modeling in text data, which may not be suitable for analyzing customer shopping patterns directly from demographics and interactions on an online shopping platform. It does not inherently provide insights into customer preferences or trends in shopping behavior.
A neural network with at least three layers could be used to model complex relationships in the data, but without specific architecture considerations (like types of layers or data preprocessing), it may not effectively capture shopping patterns. Additionally, random initial weights do not guarantee good performance and require extensive tuning and data.
Collaborative filtering is a well-established method for making recommendations based on user interactions. It identifies similarities between users and items, allowing the system to suggest products based on past behavior and preferences, making it ideal for enhancing an online shopping experience.
Random Cut Forest (RCF) is primarily used for anomaly detection in time series data and may not be the best choice for identifying customer shopping patterns. While it can provide insights into unusual behavior, it does not effectively analyze preferences and trends in customer shopping data.
I suspect that the answer is:
Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.