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Personalization, a topmost concern of modern recommendation systems (RS), is intended to predict individual motivation of a customer for this or that choice. It depends on many factors forming explicit and implicit decision context. The paper proposes RS personalization technology that focuses on ontology-based extraction of semantically interpretable context of each particular customer's decisions from his/her historical data sample with the subsequent machine learning-based extraction of customer-centered feature set and personal cause-consequence decision rules. The technology is fully implemented by Practical Reasoning, Inc. and validated via several case studies.