Context and Intention-Awareness in POIs Recommender Systems

This paper describes an agent-based approach for making context and intention-aware recommendations of Points of Interest (POI). A two-parted agent architecture was used, with an agent responsible for gathering POIs from a location-based mobile application, and a set of Personal Assistant Agents (PAA), collecting information about the context and intentions of its respective user. Each PAA includes a probabilistic classier for making recommendations given its information about the user’s context and intentions. Supervised, incremental learning occurs when the feedback of the true relevance of each recommendation is given by the user to his PAA. To evaluate the system’s recommendations, we performed an experiment based on the prole used in the training process, using dierent locations, contexts and goals.

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