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 classifier 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 profile used in the training process, using different locations, contexts and goals.


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


Recommender Systems

Related Project

Forms of Selective Attention in Intelligent Transportation Systems


6th ACM Conference on Recommender Systems (RecSys 2012), 4th Workshop on Context-Aware Recommender Systems, (CARS 2012) Dublin, Ireland. ACM, September 2012

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Year 2019 : 3 citations

 Fernandez-Rojas, R., Perry, A., Singh, H., Campbell, B., Elsayed, S., Hunjet, R., & Abbass, H. A. (2019). Contextual awareness in human-advanced-vehicle systems: A survey. IEEE Access, 7, 33304-33328.

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 Rajaonarivo, L., Fonteles, A., Sallaberry, C., Bessagnet, M. N., Roose, P., Etcheverry, P., ... & Coudert, Q. (2019). Recommendation of heterogeneous cultural heritage objects for the promotion of tourism. ISPRS International Journal of Geo-Information, 8(5), 230.

Year 2018 : 2 citations

 Portugal, I., Alencar, P., & Cowan, D. (2018). The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications, 97, 205-227.

 Wijenayake, S., Ganiachchi, S., Wijekoon, J., & Ahangama, S. (2018). Predicting Tie Strength between Facebook Friends to Improve Accuracy in Travel Recommendation Systems. In PACIS (p. 246).

Year 2017 : 2 citations

 Sassi, I. B., Mellouli, S., & Yahia, S. B. (2017). Context-aware recommender systems in mobile environment: On the road of future research. Information Systems, 72, 27-61.

 Karpus, A., Di Noia, T., & Goczy?a, K. (2017, September). Top k recommendations using contextual conditional preferences model. In 2017 Federated Conference on Computer Science and Information Systems (FedCSIS) (pp. 19-28). IEEE.

Year 2016 : 2 citations

 Karpus, A., Di Noia, T., Tomeo, P., & Goczyla, K. (2016, November). Rating Prediction with Contextual Conditional Preferences. In KDIR (pp. 419-424).

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Year 2015 : 2 citations

 Rodríguez-Hernández, María del Carmen and Ilarri, Sergio and Trillo-Lado, Raquel and Hermoso, Ramón (2015). "Location-Aware Recommendation Systems: Where We Are and Where We Recommend to Go". Proc. 9th ACM Conf. on Recommender Systems (RecSys'15), 11th Workshop on Location-Aware Recommendations (LocalRec'15). Vienna, Austria. September, 2015. pp.1-8.

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Year 2014 : 2 citations

 Riboni, Daniele and Bettini, Claudio. "Differentially-Private Release of Check-in Data for Venue Recommendation". In 12th IEEE International Conference on Pervasive Computing and Communications (PerCom'14). pp. 190-198. IEEE Computer Society. March, 2014. Budapest, Hungary.

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Year 2013 : 4 citations

 Boim, Rubi and Milo, Tova (2013). Recommendation by Examples. In 7th Int. Workshop on Personalized Access, Profile Management, and Context Awareness in Databases (PersDB'13). August, 2013, Riva del Garda, Trento, Italy

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 Riboni, Daniele and Bettini, Claudio. "A Platform for Privacy-Preserving Geo-social Recommendation of Points of Interest". 14th IEEE International Conference on Mobile Data Management (MDM'13). vol. 1, IEEE Computer Society. Milano, Italy. June, 2013. pp. 347-349.