Tasteprint: Cross-Platform Recommendation Agent

Abstract

Recommender systems model users and rank candidates within individual provider boundaries, fragmenting user context across services. User agents offer a different interaction model: they can act on the user’s behalf and seek recommendations across providers, but only if user context can travel with them. We present Tasteprint, a local-first plugin framework that combines a cold-start questionnaire with authorized activity from 13 shopping, entertainment, social, and conversational services to build portable recommendation context. Tasteprint preserves source-linked platform records, organizes them into domain summaries and a cross-domain profile, and selects task-relevant context for a host agent. This allows signals observed in one domain to inform search and recommendation in another, while profile data remains in a user-owned local directory and requires neither a new model nor a cloud profile service. The demo presents user-side cross-domain recommendation, user correction, and authorization-scoped local data collection. The demo is available at https://zaodushi.github.io/Tasteprint.skill/.