Recommending POIs Based on the User's Context and Intentions



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%.


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


Recommender Systems

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


Cited by

Year 2015 : 2 citations

 Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. "User Personality and the New User Problem in a Context-Aware Point of Interest Recommender System." Information and Communication Technologies in Tourism 2015. Springer International Publishing, 2015. 537-549.

 Kysela, Ji?í. "Analysis of privacy erosion of geosocial networks." Computational Intelligence and Informatics (CINTI), 2015 16th IEEE International Symposium on. IEEE, 2015.