Machine Learning (ML) is the most suitable AI concept in this scenario. ML focuses on developing algorithms that can learn from structured or unstructured data and make predictions based on historical patterns.
In this case, analyzing customer browsing history and purchase records falls directly under supervised learning, a subcategory of ML, which is typically used for predictive modeling in retail (such as next-best-offer, product recommendation, or demand forecasting).
According to the PECB Lead Auditor Study Guide (Domain 1), ML is specifically referenced as the core technique for prediction systems, user behavior modeling, and data-driven decision-making systems.
Though Deep Learning (DL) is a subset of ML, it is often used for more complex pattern recognition tasks such as image or speech recognition, which is not explicitly required here.
[Reference: PECB Lead Auditor Guide – Domain 1, Topic: “AI Concepts” – Table differentiating ML, DL, NLP, and Computer Vision, ISO/IEC 42001:2023 Clause 8.2.3 (Operational Planning and Control) – Emphasizes selecting AI techniques appropriate to the context and purpose, , , ]
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