Abstract
This disclosure describes an automated process for transparent algorithmic profiling and data shaping that allows an individual to actively curate environmental and physiological data to dynamically guide and personalize machine learning models. By moving beyond rigid history logs or binary input structures, the techniques of the disclosure enable a traceable, user-directed taste profile that maps real-world context onto digital preferences and actively seeds future recommendation behaviors to counteract algorithmic flattening. The disclosed system helps Artificial Intelligence (AI) models, including foundation models, generative AI models, and/or large language models (LLMs), capture and interpret multifaceted human data streams to accurately personalize user interactions. The described techniques solve preference misinterpretation (e.g., algorithmic flattening) by executing a universal gesture across platform-independent devices, which momentarily triggers a snapshot of comprehensive body sensor data (biometric context logging) and immediate ambient environmental inputs (ubiquitous sensor triggering). This multi-layered data structure generates dynamic, traceable user identity profiles, or user taste maps, that explicitly display the individual digital data sources and physical contextual observations causing specific model inferences. Furthermore, the system acts as a serendipitous recommendation engine by generating exploratory suggestion parameters that gently nudge users to try new things outside their established habits, effectively acting as an anti-echo chamber algorithm.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Strudwick, Kate and Schladow, Zoe, "CONTEXT-AWARE PERSONALIZATION", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11417