[1]   Trani, S., Losada, D. E., Lucchese, C., Perego, R., Ceccarelli, D., and Orlando, S. Sel: a unified algorithm for salient entity linking. Wiley Computational Intelligence 34, 1 (2018), 2–29. IF. 1.352. doi.

[2]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Silvestri, F., and Trani, S. X-cleaver: Learning ranking ensembles by growing and pruning trees. ACM Transactions on Intelligent Systems and Technology 9, 6 (2018), 62:1–62:26. doi.

[3]   Lettich, F., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Parallel traversal of large ensembles of decision trees. IEEE Transactions on Parallel and Distributed Systems, In Press (2018). doi.

[4]   Lulli, A., Carlini, E., Dazzi, P., Lucchese, C., and Ricci, L. Fast connected components computation in large graphs by vertex pruning. IEEE Transactions on Parallel and Distributed Systems 28, 3 (2017), 760–773. IF. 3.971. doi.

[5]   Coletto, M., Garimella, K., Gionis, A., and Lucchese, C. Automatic controversy detection in social media: a content-independent motif-based approach. Elsevier Online Social Networks and Media 3–4 (2017), 22–31. doi.

[6]   Coletto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., and Renso, C. Perception of social phenomena through the multidimensional analysis of online social networks. Elsevier Online Social Networks and Media 1 (2017), 14 – 32. doi.

[7]   Coletto, M., Aiello, L. M., Lucchese, C., and Silvestri, F. Adult content consumption in online social networks. Springer Social Network Analysis and Mining 7, 1 (2017), 28. doi.

[8]   Dato, D., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Fast ranking with additive ensembles of oblivious and non-oblivious regression trees. ACM Transactions on Information Systems 35, 2 (2016), 15:1–15:31. IF. 2.312. doi.

[9]   Capannini, G., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. Quality versus efficiency in document scoring with learning-to-rank models. Information Processing & Management (2016). IF. 2.391. doi.

[10]   Lucchese, C., Orlando, S., and Perego, R. A unifying framework for mining approximate top-k binary patterns. IEEE Transactions On Knowledge and Data Engineering 26, 12 (2014), 2900–2913. IF. 2.067. doi.

[11]   Freris, N. M., Lucchese, C., Vlachos, M., and Zoumpoulis, S. Right-protected data publishing with provable distance-based mining. IEEE Transactions On Knowledge and Data Engineering 26, 8 (2014), 2014–2028. IF. 2.067. doi.

[12]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Discovering tasks from search engine query logs. ACM Trans. Inf. Syst. 31, 3 (2013), 14. (ACM Notable Article), IF. 1.300. doi.

[13]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. Similarity caching in large-scale image retrieval. Information Processing & Management (Special Issue on Large-Scale and Distributed Systems for Information Retrieval) 48, 5 (2012), 803–818. IF. 0.817. doi.

[14]   Lucchese, C., Rayan, D., Vlachos, M., and Yu, P. S. Rights protection of trajectory datasets with nearest-neighbor preservation. VLDB Journal 19, 4 (2010), 531–556. IF. 2.198. doi.

[15]   Lucchese, C., Mastroianni, C., Orlando, S., and Talia, D. Mining@home: Towards a public resource computing framework for distributed data mining. Concurrency and Computation: Practice and Experience 22, 5 (2010), 658–682. IF. 0.907. doi.

[16]   Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubsky, J., and Zezula, P. Building a web-scale image similarity search system. Multimedia Tools and Applications 47, 3 (2010), 599–629. IF. 0.885. doi.

[17]   Kozat, S. S., Vlachos, M., Lucchese, C., Herle, H. V., and Yu, P. S. Embedding and retrieving private metadata in electrocardiograms. Journal of Medical Systems 33, 4 (2009), 241–259. IF. 0.654. doi.

[18]   Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R. A constraint-based querying system for exploratory pattern discovery. Information Systems 34, 1 (2009), 3–27. IF. 1.966. doi.

[19]   Bonchi, F., and Lucchese, C. Extending the state-of-the-art of constraint-based pattern discovery. Data and Knowledge Engineering 60, 2 (2007), 377–399. IF. 1.144. doi.

[20]   Lucchese, C., Orlando, S., and Perego, R. Fast and memory efficient mining of frequent closed itemsets. IEEE Transactions On Knowledge and Data Engineering 18, 1 (2006), 21–36. IF. 2.063. doi.

[21]   Bonchi, F., and Lucchese, C. On condensed representations of constrained frequent patterns. Knowledge and Information Systems 9, 2 (2006), 180–201. IF. 0.833. doi.

[22]   Lucchese, C., Nardini, F. M., Pasumarthi, R. K., Bruch, S., Bendersky, M., Wang, X., Oosterhuis, H., Jagerman, R., and de Rijke, M. Learning to rank in theory and practice: From gradient boosting to neural networks and unbiased learning. In SIGIR ’19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (2019), ACM, pp. 1419–1420. (Tutorial). doi.

[23]   Calzavara, S., Lucchese, C., and Tolomei, G. Adversarial training of gradient-boosted decision trees. In CIKM ’19: Proceedings of the The 28th ACM International Conference on Information and Knowledge Management (2019). (Short), (acceptance 21.3%).

[24]   Lucchese, C., Nardini, F. M., Perego, R., Trani, R., and Venturini, R. Efficient and effective query expansion for web search. In CIKM ’18: Proceedings of the The 27th ACM International Conference on Information and Knowledge Management (2018). (Short), (acceptance 23.4%).

[25]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Trani, S. Selective gradient boosting for effective learning to rank. In SIGIR ’18: Proceedings of the 41th International ACM SIGIR Conference on Research and Development in Information Retrieval (2018). (acceptance 21%).

[26]   Lucchese, C., and Nardini, F. M. Efficiency/effectiveness trade-offs in learning to rank. In ECML PKDD ’18: Machine Learning and Knowledge Discovery in Databases - European Conference (2018). (Tutorial).

[27]   Ferro, N., Lucchese, C., Maistro, M., and Perego, R. Continuation methods and curriculum learning for learning to rank. In CIKM ’18: Proceedings of the The 27th ACM International Conference on Information and Knowledge Management (2018). (Short), (acceptance 23.4%).

[28]   Ruback, L., Casanova, M. A., Lucchese, C., and Renso, C. SELEcTor: Discovering similar entities on linked data by ranking their features. In ICSC ’17: IEEE International Conference on Semantic Computing (2017). (acceptance 20%).

[29]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Trani, S. X-dart: Blending dropouts and pruning for efficient learning to rank. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017). (Short), (acceptance 30%).

[30]   Lucchese, C., and Nardini, F. M. Efficiency/effectiveness trade-offs in learning to rank. In ICTIR ’17: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, Amsterdam, The Netherlands (2017), ACM, pp. 329–330. (Tutorial). doi.

[31]   Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., and Trani, S. Rankeval: An evaluation and analysis framework for learning-to-rank solutions. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017). (Demo), (acceptance 47%).

[32]   Ferro, N., Lucchese, C., Maistro, M., and Perego, R. On including the user dynamic in learning to rank. In SIGIR ’17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (2017). (Short), (acceptance 30%).

[33]   Coletto, M., Garimella, K., Gionis, A., and Lucchese, C. A motif-based approach for identifying controversy. In ICWSM ’17: International AAAI Conference on Web and Social Media (2017). (Short).

[34]   Zneika, M., Lucchese, C., Vodislav, D., and Kotzinos, D. Summarizing linked data rdf graphs using approximate graph pattern mining. In EDBT ’16: Proceedings of the 19th International Conference on Extending Database Technology (2016). (poster).

[35]   Lucchese, C., Orlando, S., and Perego, R. Evaluating top-k approximate patterns via text clustering. In DAWAK ’16: 18th International Conference on Data Warehousing and Knowledge Discovery (2016).

[36]   Lucchese, C., Nardini, F. M., Orlando, S., and Tolomei, G. Learning to rank user queries to detect search tasks. In ICTIR ’16: International Conference on the Theory of Information Retrieval (2016).

[37]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Exploiting cpu simd extensions to speed-up document scoring with tree ensembles. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016). (Short).

[38]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Silvestri, F., and Trani, S. Post-learning optimization of tree ensembles for efficient ranking. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016). (Short).

[39]   Gigli, A., Lucchese, C., Nardini, F. M., and Perego, R. Fast feature selection for learning to rank. In ICTIR ’16: International Conference on the Theory of Information Retrieval (2016). (Short).

[40]   Coletto, M., Lucchese, C., Orlando, S., and Perego, R. Polarized user and topic tracking in twitter. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (2016). (Short).

[41]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. SEL: a unified algorithm for entity linking and saliency detection. In DocEng ’16: Proceedings of the 2015 ACM Symposium on Document Engineering (2016). ((best student paper)).

[42]   Aiello, L. M., Coletto, M., Lucchese, C., and Silvestri, F. On the behaviour of deviant communities in online social networks. In ICWSM ’16: International AAAI Conference on Web and Social Media (2016).

[43]   Lucchese, C., Orlando, S., and Perego, R. Supervised evaluation of top-k itemset mining algorithms. In DAWAK ’15: 17th International Conference on Data Warehousing and Knowledge Discovery (2015).

[44]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Quickscorer: a fast algorithm to rank documents with additive ensembles of regression trees. In SIGIR ’15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015). (best paper) (ACM Notable Article).

[45]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. Speeding up document ranking with rank-based features. In SIGIR ’15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015). (Short).

[46]   Coletto, M., Lucchese, C., Orlando, S., Perego, R., Chessa, A., and Puliga, M. Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study. In IC2S2 ’15: Proceedings of the International Conference on Computational Social Science (2015). (poster).

[47]   Carlini, E., Dazzi, P., Lulli, A., Lucchese, C., and Ricci, L. Cracker: Crumbling large graphs into connected components. In ISCC ’15: Proceedings of the 20th IEEE Symposium on Computers and Communications (2015).

[48]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Manual annotation of semi-structured documents for entity-linking. In CIKM ’14: Proceedings of the 23rd ACM Intl. Conference on Information and Knowledge Management (2014), pp. 2075–2077. (Demo).

[49]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Dexter 2.0 – an open source tool for semantically enriching data. In ISWC ’14: Proceedings of the 13th Intl. Semantic Web Conference (2014), pp. 417–420. (Demo).

[50]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Modeling and predicting the task-by-task behavior of search engine users. In OAIR ’13: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (2013), pp. 77–84.

[51]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Learning relatedness measures for entity linking. In CIKM ’13: Proceedings of the 22st ACM international conference on Information and knowledge management (2013). (acceptance 16.9%). doi.

[52]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., and Perego, R. When entities meet query recommender systems: semantic search shortcuts. In Proceedings of the 28th Annual ACM Symposium on Applied Computing (2013), pp. 933–938.

[53]   Morales, G. D. F., Gionis, A., and Lucchese, C. From chatter to headlines: Harnessing the real-time web for personalized news recommendation. In WSDM ’12: ACM International Conference on Web Search and Data Mining (2012). (acceptance 20.7%). doi.

[54]   Lucchese, C., Perego, R., Silvestri, F., Vahabi, H., and Venturini, R. How random walks can help tourism. In ECIR ’12: Proceedings of the 34th European conference on Advances in Information Retrieval (2012), pp. 195–206. (Short), (acceptance 21%). doi.

[55]   Blanco, R., Ceccarelli, D., Lucchese, C., Perego, R., and Silvestri, F. You should read this! let me explain you why: explaining news recommendations to users. In CIKM ’12: Proceedings of the 21st ACM international conference on Information and knowledge management (2012), pp. 1995–1999. (Short), (acceptance 27.8%). doi.

[56]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Identifying task-based sessions in search engine query logs. In WSDM ’11: ACM International Conference on Web Search and Data Mining (February 2011). (best 6), (acceptance 22%). doi.

[57]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. Caching query-biased snippets for efficient retrieval. In EDBT ’11: ACM 14th International Conference on Extending Database Technology (2011), pp. 93–104.

[58]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., and Tolomei, G. Improving europeana search experience using query logs. In TPDL ’11: International Conference on Theory and Practice of Digital Libraries (Septemebr 2011).

[59]   Boley, M., Lucchese, C., Paurat, D., and Gärtner, T. Direct local pattern sampling by efficient two-step random procedures. In KDD ’11: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August 21-24 2011), pp. 582–590. (VQR: 0.7), (acceptance 7.8%). doi.

[60]   Lucchese, C., Orlando, S., and Perego, R. Mining top-k patterns from binary datasets in presence of noise. In SDM ’10: Proceedings of the 2010 SIAM International Conference on Data Mining (April 2010). (best 12), (VQR: 1), (acceptance 24%). doi.

[61]   Lucchese, C., Orlando, S., and Perego, R. A generative pattern model for mining binary datasets. In SAC ’10: Proceedings of the 25^th ACM Symposium on Applied Computing (March 2010). (poster), (acceptance 33%). doi.

[62]   Baraglia, R., Lucchese, C., and De Francisci Morales, G. Document similarity self-join with mapreduce. In ICDM ’10: Proceedings of the tenth IEEE International Conference on Data Mining (December 2010). (Short), (acceptance 19%). doi.

[63]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. Caching content-based queries for robust and efficient image retrieval. In EDBT ’09: Proceedings of the twelfth International Conference on Extending Database Technology (March 2009), pp. 780–790. (VQR: 0.8), (acceptance 32%). doi.

[64]   Lucchese, C., Rayan, D., Vlachos, M., and Yu, P. S. Rights protection of multidimensional time-series datasets with neighborhood preservation. In ICDE ’08: Proceedings of the 2008 IEEE International Conference on Data Engineering (2008), pp. 1349–1351. (poster), (acceptance 31%). doi.

[65]   Lucchese, C., Rayan, D., Vlachos, M., and Yu, P. S. Ownership protection of shapes with geodesic distance preservation. In EDBT ’08: Proceedings of the eleventh International Conference on Extending Database Technology (2008), pp. 276–286. (VQR: 0.8), (acceptance 17%). doi.

[66]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. A metric cache for similarity search. In Proceedings of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval (2008), ACM, pp. 43–50.

[67]   Baraglia, R., Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. (Query) History teaches everything, including the future. In LA-WEB ’08: Proceedings of the sixth Latin American Web Congress (Invited) (2008), pp. 12–22. (invited). doi.

[68]   Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. Mining query logs to optimize index partitioning in parallel web search engines. In INFOSCALE ’07: Proceedings of the Second International Conference on Scalable Information Systems (June 2007). (acceptance 16%). doi.

[69]   Lucchese, C., Orlando, S., and Perego, R. Parallel mining of frequent closed patterns: Harnessing modern computer architectures. In ICDM ’07: Proceedings of the Seventh IEEE International Conference on Data Mining (November 2007), pp. 242–251. (VQR: 1), (acceptance 7%). doi.

[70]   Lucchese, C., Orlando, S., and Perego, R. Mining frequent closed itemsets out of core. In SDM ’06: Proceedings of the third SIAM International Conference on Data Mining (April 2006). (acceptance 16%). doi.

[71]   Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R. Conquest: a constraint-based querying system for exploratory pattern discovery. In ICDE ’06: Proceedings of the 2006 IEEE International Conference on Data Engineering (Demo) (2006), pp. 159–160.

[72]   Baraglia, R., Lucchese, C., Orlando, S., Serranò, M., and Silvestri, F. A privacy preserving web recommender system. In SAC ’06: Proceedings of the 21st ACM Symposium on Applied Computing (April 2006), pp. 559–563. (acceptance 32%). doi.

[73]   Bonchi, F., and Lucchese, C. Pushing tougher constraints in frequent pattern mining. In PAKDD ’05: Proceedings of the Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (Hanoi, Vietnam, May 2005), pp. 114–124. (acceptance 15%). doi.

[74]   Bonchi, F., and Lucchese, C. On closed constrained frequent pattern mining. In ICDM ’04: Proceedings of the Fourth IEEE International Conference on Data Mining (November 2004), pp. 35–42. (acceptance 9%). doi.

[75]   Coletto, M., and Lucchese, C. Social–Spatiotemporal Analysis of Topical and Polarized Communities in Online Social Networks. Springer New York, New York, NY, 2017, pp. 1–16.

[76]   Coletto, M., Lucchese, C., and Orlando, S. Do violent people smile: Social media analysis of their profile pictures. In Companion of the The Web Conference 2018 on The Web Conference 2018, WWW 2018, Lyon , France, April 23-27, 2018 (2018), ACM, pp. 1465–1468. doi.

[77]   Pollacci, L., Sîrbu, A., Giannotti, F., Pedreschi, D., Lucchese, C., and Muntean, C. I. Sentiment spreading: An epidemic model for lexicon-based sentiment analysis on twitter. In AI*IA 2017 Advances in Artificial Intelligence - XVIth International Conference of the Italian Association for Artificial Intelligence, Bari, Italy, November 14-17, 2017, Proceedings (2017), vol. 10640 of Lecture Notes in Computer Science, Springer, pp. 114–127. doi.

[78]   Lucchese, C., Nardini, F. M., Perego, R., and Trani, S. The impact of negative samples on learning to rank. In Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers co-located with the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR 2017), Amsterdam, The Netherlands, October 1, 2017. (2017), CEUR Workshop Proceedings, CEUR-WS.org.

[79]   Lettich, F., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Multicore/manycore parallel traversal of large forests of regression trees. In 2017 International Conference on High Performance Computing & Simulation, HPCS 2017, Genoa, Italy, July 17-21, 2017 (2017), IEEE, p. 915. doi.

[80]   Ferro, N., Lucchese, C., Maistro, M., and Perego, R. Report on LEARNER 2017: 1st international workshop on learning next generation rankers. SIGIR Forum 51, 3 (2017), 145–151. doi.

[81]   Ferro, N., Lucchese, C., Maistro, M., and Perego, R. Learning next generation rankers (LEARNER 2017). In Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2017, Amsterdam, The Netherlands, October 1-4, 2017 (2017), ACM, pp. 331–332. doi.

[82]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Speeding-up document scoring with tree ensembles using cpu simd extensions. IIR ’16: 7th Italian Information Retrieval Workshop (2016).

[83]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Ranking documents efficiently with quickscorer. In SEBD ’16: Proceedings of the 24th Italian Symposium on Advanced Database Systems (June 2016).

[84]   Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Silvestri, F., and Trani, S. Post-learning optimization of tree ensembles. IIR ’16: 7th Italian Information Retrieval Workshop (2016).

[85]   Lettich, F., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. Gpu-based parallelization of quickscorer to speed-up document ranking with tree ensembles. IIR ’16: 7th Italian Information Retrieval Workshop (2016).

[86]   Coletto, M., Esuli, A., Lucchese, C., Muntean, C. I., Nardini, F. M., Perego, R., and Renso, C. Sentiment-enhanced multidimensional analysis of online social networks: Perception of the mediterranean refugees crisis. SNAST ’16: Workshop on Social Network Analysis Surveillance Technologies, colocated with ASONAM ’16: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2016).

[87]   Zneika, M., Lucchese, C., Vodislav, D., and Kotzinos, D. Rdf graph summarization based on approximate patterns. In ISIP ’15: PostProceeding of the 9th International Workshop on Information Search, Integration and Personalization (2015), Springer CCIS Series.

[88]   Coletto, M., Lucchese, C., Orlando, S., Perego, R., Chessa, A., and Puliga, M. Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study. ISTI 2015-TR-009: Poster accepted at ICCSS 2015 (2015).

[89]   Coletto, M., Lucchese, C., Orlando, S., and Perego, R. Electoral predictions with twitter: a machine-learning approach. IIR ’15: 6th Italian Information Retrieval Workshop (2015).

[90]   Capannini, G., Dato, D., Lucchese, C., Mori, M., Nardini, F. M., Orlando, S., Perego, R., and Tonellotto, N. QuickRank: a C++ suite of learning to rank algorithms. IIR ’15: 6th Italian Information Retrieval Workshop (2015).

[91]   Lucchese, C., Muntean, C. I., Perego, R., Silvestri, F., Vahabi, H., and Venturini, R. Recommender systems. Mining User Generated Content – Social Media and Social Computing (2014), 287–317.

[92]   Lucchese, C., Perego, R., Trani, S., Atzemoglou, M., Baurens, B., and Kotzinos, D. Ingeoclouds: A cloud-based platform for sharing geodata across europe. ERCIM News 2013, 94 (2013).

[93]   Lagarde, P., and Lucchese, C. From a classical web mapping publication to a inspire service architecture in the cloud. INSPIRE Conference (2013).

[94]   Ceccarelli, D., Lucchese, C., Orlando, S., and Tolomei, G. Twitter anticipates bursts of requests for wikipedia articles. In Workshop on Data-driven User Behavioral Modelling and Mining from Social Media (2013).

[95]   Ceccarelli, D., Lucchese, C., Orlando, S., Perego, R., and Trani, S. Dexter: an open source framework for entity linking. In Sixth International Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR) (2013).

[96]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., and Perego, R. On suggesting entities as web search queries. In Proceedings of the 4th Italian Information Retrieval Workshop (2013), pp. 37–40.

[97]   Ceccarelli, D., Gordea, S., Lucchese, C., Nardini, F. M., Perego, R., and Tolomei, G. Discovering europeana users’ search behavior. ERCIM News 2011, 86 (2011).

[98]   Boley, M., Lucchese, C., Paurat, D., and Gartner, T. Direct pattern sampling with respect to pattern frequency. KDML ’11: Workshop on Knowledge Discovery, Data Mining and Machine Learning, in conjunction with the LWA 2011 (September 2011).

[99]   Boley, M., Lucchese, C., Paurat, D., and Gärtner, T. Direct pattern sampling with respect to pattern frequency. In KDML ’11: Workshop on Knowledge Discovery, Data Mining and Machine Learning (September 2011).

[100]   Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. Detecting task-based query sessions using collaborative knowledge. In IWI ’10: International Workshop on Intelligent Web Interaction (August 2010).

[101]   Cambazoglu, B. B., and Lucchese, C. LSDS-IR ’11: 9th workshop on large-scale distributed systems for information retrieval. CIKM ’11: the 20th ACM Conference on Information and Knowledge Management 44, 2 (2010), 54–58.

[102]   Blanco, R., Cambazoglu, B. B., and Lucchese, C. LSDS-IR ’10: 8th workshop on large-scale distributed systems for information retrieval. SIGIR Forum 44, 2 (2010), 54–58.

[103]   Baraglia, R., Lucchese, C., Orlando, S., Perego, R., and Silvestri, F. Preserving privacy in web recommender systems. In Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques. CRC, Taylor and Francis Group, LLC., 2010.

[104]   Baraglia, R., Lucchese, C., and De Francisci Morales, G. Scaling out all pairs similarity search with mapreduce. In LSDS-IR ’10: Proceedings of the eighth Workshop on Large-Scale Distributed Systems for Information Retrieval (July 2010).

[105]   Lucchese, C., Skobeltsyn, G., and Yee, W. G. LSDS-IR ’09: 7th workshop on large-scale distributed systems for information retrieval. SIGIR Forum 43, 2 (2009), 34–40. doi.

[106]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. Caching algorithms for similarity search. In SEBD ’09: Proceedings of the Seventeenth Italian Symposium on Advanced Database Systems (June 2009).

[107]   Bolettieri, P., Falchi, F., Lucchese, C., Mass, Y., Perego, R., Rabitti, F., and Shmueli-Scheuer, M. Searching 100M images by content similarity. In IRCDL ’09: Post-proceedings of the 5th Italian Research Conference on Digital Library Systems, revised selected papers. (January 2009), DELOS: an Association for Digital Libraries, pp. 88–89.

[108]   Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., and Rabitti, F. Enabling content-based image retrieval in very large digital libraries. In Second Workshop on Very Large Digital Libraries (VLDL 2009), 2 October 2009, Corfu, Greece (Pisa, Italy, 2009), DELOS, pp. 43–50.

[109]   Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., and Rabitti, F. CoPhIR: a test collection for content-based image retrieval. CoRR abs/0905.4627v2 (2009).

[110]   Falchi, F., Lucchese, C., Orlando, S., Perego, R., and Rabitti, F. A metric cache for similarity search. In LSDS-IR ’08: Proceedings of the sixth Workshop on Large-Scale Distributed Systems for Information Retrieval (October 2008). ((VQR: 0.5))).

[111]   Falchi, F., and Lucchese, C. Special track on Engineering Large-Scale Distributed Systems: editorial message. In SAC ’08: Proceedings of the 2008 ACM Symposium on Applied Computing (March 2008), vol. 1, ACM, pp. 453–454.

[112]   Buehrer, G., Coppola, M., and Lucchese, C. HPDM ’08: Workshop on high performance data mining. In ICDM ’08: Proceedings of the Ninth IEEE International Conference on Data Mining (2008), IEEE Computer Society, pp. xxvii–xxix.

[113]   Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubský, J., and Zezula, P. Crawling, indexing, and similarity searching images on the web. In SEBD ’08: Proceedings of the Sixteenth Italian Symposium on Advanced Database Systems (June 2008), pp. 382–389.

[114]   Barbalace, D., Lucchese, C., Mastroianni, C., Orlando, S., and Talia, D. Mining@home: Public resource computing for distributed data mining. In Proceeding of CoreGRID Symposium 2008 (August 2008).

[115]   Lucchese, C., Orlando, S., Perego, R., and Silvestri, C. Mining frequent closed itemsets from distributed repositories. In Knowledge and Data Managment in GRIDS. CoreGRID Series by Springer, 2007.

[116]   Laforenza, D., Lucchese, C., Orlando, S., Perego, R., Puppin, D., and Silvestri, F. On the value of query logs for modern information retrieval. In DART ’06: Distributed Agent-based Retrieval Tools, The Future of Search Engines’ Technologies. Polimetrica, 2006, pp. 123–147.

[117]   Bonchi, F., Lucchese, C., Giannotti, F., Orlando, S., Perego, R., and Trasarti, R. On interactive pattern mining from relational databases. In SEBD ’06: Proceedings of the Fourteenth Italian Symposium on Advanced Database Systems (June 2006), pp. 329–338.

[118]   Bonchi, F., Giannotti, F., Lucchese, C., Orlando, S., Perego, R., and Trasarti, R. On interactive pattern mining from relational databases. In KDID ’06: Knowledge Discovery in Inductive Databases, Revised Selected and Invited Papers. Lecture Notes in Computer Science by Springer, 2006.

[119]   Lucchese, C., Orlando, S., and Perego, R. On distributed closed itemsets mining: some preliminary results. In HPDM ’05: Proceedings of the eight SIAM SDM 2004 Workshop on High Performace Distributed Data Mining (April 2005), pp. 562–567.

[120]   Lucchese, C., Orlando, S., and Perego, R. WebDocs: a real-life huge transactional datase. In FIMI ’04: Proceedings of the ICDM 2004 Workshop on Frequent Itemset Mining Implementations (November 2004).

[121]   Lucchese, C., Orlando, S., and Perego, R. kDCI: on using direct count up to the third iteration. In FIMI ’04: Proceedings of the ICDM 2004 Workshop on Frequent Itemset Mining Implementations (November 2004).

[122]   Lucchese, C., Orlando, S., and Perego, R. DCI_Closed: A fast and memory efficient algorithm to mine frequent closed itemsets. In FIMI ’04: Proceedings of the ICDM 2004 Workshop on Frequent Itemset Mining Implementations (November 2004).

[123]   Lucchese, C., Orlando, S., Palmerini, P., Perego, R., and Silvestri, F. kDCI: a multi-strategy algorithm for mining frequent sets. In FIMI ’03: Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations (November 2003).

[124]   Dato, D., Lucchese, C., Nardini, F. M., Orlando, S., Perego, R., Tonellotto, N., and Venturini, R. A method to rank documents by a computer, using additive ensembles of regression trees and cache optimization, and search engine using such a method. Tiscali S.p.A. PCT29914, (Application) (2015).