Abstract
A framework and methodology are described for enhancing personalized information retrieval and ranking through a multi-scale memory architecture. The system captures and synthesizes user behavior across different time horizons, utilizing distinct short-term, long-term, and temporal memory modules. The architecture processes the composite memory state to compute various personalization levels, allowing for granular, controllable, and compliance-aware user specification in real-time content delivery.
Keywords: Machine Learning, Memory Architecture, User Intent Modeling, Personalization Control, Temporal Decay, Information Retrieval, Real-Time Prediction.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Yavary, Aura, "Decoupled Multi-Scale Memory Architecture for Adaptive User Intent Modeling and Real-Time Personalization Control", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9744