Rule-based personalization framework for integrating recommendation systems

This invention describes a computer system that combines different ways of suggesting things to users. It uses a set of rules to decide which specific recommendation method to use at any given time. These rules can trigger other, more complex recommendation engines, like those based on data analysis or machine learning, and feed them the necessary information.

Why it matters: Filed before modern AI and machine learning models became the dominant force in recommendation systems. The framework's ability to integrate diverse 'non-rule-based systems' is now highly relevant for orchestrating today's sophisticated AI-driven recommenders.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedComplex software logic, integration of multiple systems

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