Recommending POIs Based on the User's Context and Intentions

Authors

Abstract

This paper describes a Recommender System that implements a Multiagent System for making personalised context and intention-aware recommendations of Points of Interest (POIs). A two-parted agent architecture was used, with an agent responsible for gathering POIs from a location-based service, and a set of Personal Assistant Agents (PAAs) collecting information about the context and intentions of its respective user. In each PAA were embedded four Machine Learning algorithms, with the purpose of ascertaining how well-suited these classifiers are for filtering irrelevant POIs, in a completely automatic fashion. Supervised, incremental learning occurs when the feedback on the true relevance of each recommendation is given by the user to his PAA. To evaluate the recommendations’ accuracy, we performed an experiment considering three types of users, using different contexts and intentions. As a result, all the PAA had high accuracy, revealing in specific situations F1 scores higher than 87%.

Keywords

Context, Information Overload, Machine Learning, Personal Assistant Agents, Points of Interest Recommendation, User Modeling.

Subject

Recommender Systems

Related Project

Forms of Selective Attention in Intelligent Transportation Systems

Conference

11th International Conference on Practical Applications of Agents and Multi-Agent Systems, Salamanca, Spain, May 2013

DOI