perSM

perSM, short for personalized semantic mapping, is a framework used in information retrieval and AI to construct user-specific semantic spaces. It combines a global semantic graph—derived from large corpora and knowledge bases—with a lightweight, privacy-preserving user embedding to tailor representations of concepts to an individual’s interests and context. The goal is to improve relevance and explainability in tasks such as search, summarization, and recommendations.

In typical implementations, perSM operates in two stages. First, a base semantic graph is built from sources such as text corpora, ontologies, and knowledge graphs to encode relationships among concepts. Second, a user-specific projection adapts this graph to the individual by learning a user embedding from interaction signals (queries, clicks, dwell time) and possibly demographic or contextual data. Graph neural networks or transformer-based encoders propagate user signals across the graph, producing a personalized semantic space that emphasizes relations relevant to the user.

perSM can improve personalized search and recommendations, enable adaptive summarization and question answering, and support accessible interfaces by reweighting concepts according to user needs.

Limitations and challenges include cold-start for new users, privacy and data minimization concerns, and the risk of reinforcing biases or echo chambers if signals are not carefully managed. Computational cost is nontrivial due to graph processing and model updates.

The term perSM has appeared in theoretical discussions and some early systems since the mid-2020s, with ongoing research into standards for interoperability and privacy-preserving learning. See also semantic embedding, knowledge graphs, graph neural networks, and personalized recommender systems.