Method and system for user profiling for content recommendation

This invention describes a way to create detailed user profiles for recommending content. It works by taking information about what a user does, finding key items or concepts (entities) within that information, and then expanding on those concepts using additional knowledge to create a richer set of ideas. These expanded ideas are then organized into categories to build a comprehensive profile that helps personalize content suggestions.

Why it matters: Filed before the widespread adoption of large language models (LLMs), the core task of identifying and augmenting entities with semantic knowledge was significantly more complex. Modern LLMs and advanced NLP techniques can now efficiently perform the entity recognition, inference, and augmentation steps described in the claims, making the system much more robust and easier to implement than in 2016.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware system, data processing, machine learning algorithms

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