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R. Guidotti, Monreale, A., Nanni, M., Giannotti, F., and Pedreschi, D., “Clustering Individual Transactional Data for Masses of Users”, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017.
R. Guidotti and Coscia, M., “On the Equivalence Between Community Discovery and Clustering”, in International Conference on Smart Objects and Technologies for Social Good, 2017.
R. Guidotti, Coscia, M., Pedreschi, D., and Pennacchioli, D., “Behavioral Entropy and Profitability in Retail”, in IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), Paris, 2015.
R. Guidotti, Nanni, M., Rinzivillo, S., Pedreschi, D., and Giannotti, F., “Never drive alone: Boosting carpooling with network analysis”, Information Systems, vol. 64, pp. 237–257, 2017.
R. Guidotti, Gabrielli, L., Monreale, A., Pedreschi, D., and Giannotti, F., “Discovering temporal regularities in retail customers’ shopping behavior”, EPJ Data Science, vol. 7, p. 6, 2018.
R. Guidotti, Sassi, A., Berlingerio, M., Pascale, A., and Ghaddar, B., “Social or green? A data-driven approach for more enjoyable carpooling”, in Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, 2015.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., “Next Basket Prediction using Recurring Sequential Patterns”, arXiv preprint arXiv:1702.07158, 2017.
R. Guidotti, Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., and Giannotti, F., “Local Rule-Based Explanations of Black Box Decision Systems”, 2018.
R. Guidotti and Cintia, P., “Towards a Boosted Route Planner Using Individual Mobility Models”, in Software Engineering and Formal Methods, Springer Berlin Heidelberg, 2015, pp. 108–123.
R. Guidotti, Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., and Turini, F., “Factual and Counterfactual Explanations for Black Box Decision Making”, IEEE Intelligent Systems, 2019.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., “Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences”, IEEE Transactions on Knowledge and Data Engineering, 2018.
R. Guidotti and Ruggieri, S., “Assessing the Stability of Interpretable Models”, arXiv preprint arXiv:1810.09352, 2018.
R. Guidotti, Soldani, J., Neri, D., and Brogi, A., “Explaining successful docker images using pattern mining analysis”, in Federation of International Conferences on Software Technologies: Applications and Foundations, 2018.
R. Guidotti, Soldani, J., Neri, D., Brogi, A., and Pedreschi, D., “Helping your docker images to spread based on explainable models”, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2018.
R. Guidotti, Monreale, A., Rinzivillo, S., Pedreschi, D., and Giannotti, F., “Retrieving Points of Interest from Human Systematic Movements”, in Software Engineering and Formal Methods, Springer International Publishing, 2014, pp. 294–308.
R. Guidotti, Monreale, A., Matwin, S., and Pedreschi, D., “Black Box Explanation by Learning Image Exemplars in the Latent Feature Space”, in Machine Learning and Knowledge Discovery in Databases, Cham, 2020.
R. Guidotti, “Mobility Ranking - Human Mobility Analysis Using Ranking Measures”, 2013.
R. Guidotti and Rossetti, G., ““Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network”, in Formal Methods. FM 2019 International Workshops, Cham, 2020.
R. Guidotti, Rossetti, G., Pappalardo, L., Giannotti, F., and Pedreschi, D., “Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences”, in 2017 IEEE International Conference on Data Mining (ICDM), 2017.
R. Guidotti, Monreale, A., and Pedreschi, D., “The AI black box Explanation Problem”, ERCIM NEWS, pp. 12–13, 2019.
R. Guidotti, Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D., “A survey of methods for explaining black box models”, ACM computing surveys (CSUR), vol. 51, p. 93, 2018.
R. Guidotti, Monreale, A., Rinzivillo, S., Pedreschi, D., and Giannotti, F., “Unveiling mobility complexity through complex network analysis”, Social Network Analysis and Mining, vol. 6, p. 59, 2016.
R. Guidotti, Monreale, A., and Cariaggi, L., “Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers”, in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2019.
R. Guidotti, Trasarti, R., Nanni, M., Giannotti, F., and Pedreschi, D., “There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas”, in 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), Tokyo, 2017.
R. Guidotti and Ruggieri, S., “On The Stability of Interpretable Models”, in 2019 International Joint Conference on Neural Networks (IJCNN), 2019.

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