Which combination of algorithms should the data scientist use to meet this requirement?

Explanations:

Latent Dirichlet Allocation (LDA) is a topic modeling technique for identifying thematic structures in text data, not for customer segmentation.

K-means is an unsupervised clustering algorithm well-suited for identifying groups of customers based on similarities, enabling segmentation for targeted marketing.

Semantic segmentation is used in image processing for pixel-level classification; it is not applicable to customer segmentation in structured data.

Principal Component Analysis (PCA) is a dimensionality reduction technique that helps reduce the large dataset to fewer features, making clustering algorithms like K-means more efficient and effective.

Factorization Machines (FM) are primarily used for predictive modeling, especially in recommendation systems, not for unsupervised customer segmentation.