Books and monographs

• L. Boratto, S. Faralli, M. Marras, and G. Stilo, “Bias and SociAdvances in Bias and Fairness in Information Retrieval, 4th International Workshop, BIAS 2023,” Springer Communications in Computer and Information Science, 2023.
• L. Boratto, S. Faralli, M. Marras, and G. Stilo, “Bias and SociAdvances in Bias and Fairness in Information Retrieval, Third International Workshop, BIAS 2022,” Springer Communications in Computer and Information Science, 2022.
• L. Boratto, S. Faralli, M. Marras, and G. Stilo, “Bias and SociAdvances in Bias and Fairness in Information Retrieval, Second International Workshop, BIAS 2021,” Springer Communications in Computer and Information Science, 2021.
• L. Boratto, S. Faralli, M. Marras, and G. Stilo, “Bias and Social Aspects in Search and Recommendation,” Springer Communications in Computer and Information Science, 2020.
• A. Felfernig, L. Boratto, M. Stettinger, and M. Tkalčič, “Group Recommender Systems – An Introduction,” Springer Briefs in Electrical and Computer Engineering, 2018.

Papers by topic

Explainable recommendation

• A. Ariza-Casabona, M. Salamó, L. Boratto, G. Fenu, “Towards self-explaining sequence-aware
  recommendation,” in Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pp. 904–911. ACM (2023).
• G. Balloccu, L. Boratto, C. Cancedda, G. Fenu, M. Marras, “Knowledge is power, understanding is impact: Utility and beyond goals, explanation quality, and fairness in path reasoning recommendation“. In: Advances in Information Retrieval – 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part III, Lecture Notes in Computer Science, vol. 13982, pp. 3-19. Springer (2023).
• G. Balloccu, L. Boratto, G. Fenu, M. Marras, “Post processing recommender systems with knowledge graphs for recency, popularity, and diversity of explanations“, in SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 – 15, 2022, pp. 646-656. ACM (2022).
• G. Balloccu, L. Boratto, G. Fenu, M. Marras, “Recency, popularity, and diversity of explanations in knowledge-based recommendation“, in Proceedings of the 12th Italian Information Retrieval Workshop 2022, Milan, Italy, June 29-30, 2022, CEUR Workshop Proceedings, vol. 3177. CEUR-WS.org (2022).

Multi-objective recommendation

• V. Paparella, V.W. Anelli, L. Boratto, T. Di Noia, “Reproducibility of multi-objective reinforcement
  learning recommendation: Interplay between effectiveness and beyond-accuracy perspectives,” in
  Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore,
  Singapore, September 18-22, 2023, pp. 467–478. ACM (2023).
• P. Dokoupil, L. Peska, L. Boratto, “Looks can be deceiving: Linking user-item interactions and user’s propensity towards multi-objective recommendations,” in Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023, Singapore, Singapore, September 18-22, 2023, pp. 912–918. ACM (2023).

Algorithmic bias and fairness in ranking and recommendation

• L. Boratto, F. Fabbri, G. Fenu, M. Marras, G. Medda, “Counterfactual graph augmentation for consumer unfairness mitigation in recommender systems”, in Proceedings of the 32nd {ACM} International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023, pp. 3753–3757. ACM (2023).
• P. Dokoupil, L. Peska, L. Boratto, “Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pp. 2354–2358. ACM (2023).
• M. Abdelrazek, E. Purificato, L. Boratto, E.W. De Luca, “Fairup: A framework for fairness analysis of graph neural network-based user profiling models,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pp. 3165–3169. ACM (2023).
• P. Sánchez, A. Bellogín, L. Boratto, “Bias characterization, assessment, and mitigation in location-based recommender systems“. Data Min. Knowl. Discov. 37(5), 1885–1929 (2023).
• L. Boratto, G. Fenu, M. Marras, G. Medda, “Practical perspectives of consumer fairness in recommendation“. Inf. Process. Manag, vol. 60, no. 2, (2023).
• E. Purificato, L. Boratto, E.W. De Luca, “Do graph neural networks build fair user models? assessing disparate impact and mistreatment in behavioural user profiling“, in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022, pp. 4399–4403. ACM (2022).
• M. Marras, L. Boratto, G. Ramos, G. Fenu, “Regulating group exposure for item providers in recommendation“, in SIGIR ’22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 – 15, 2022, pp. 1839–1843. ACM (2022).
• L. Boratto, G. Fenu, M. Marras, G. Medda, “Consumer fairness in recommender systems: Contextualizing definitions and mitigations“, in Advances in Information Retrieval – 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10-14, 2022, Proceedings, Part I, Lecture Notes in Computer Science, vol. 13185, pp. 552–566. Springer (2022).
• G. Ramos, L. Boratto, M. Marras, “Robust reputation independence in ranking systems for multiple sensitive attributes“. Mach. Learn. 111(10), 3769-3796 (2022).
• J. Saúde, G. Ramos, L. Boratto, and C. Caleiro, “A robust reputation-based group ranking system and its resistance to bribery,” ACM Trans. Knowl. Discov. Data, vol. 16, no. 2, pp. 26:1–26:35, 2022.
• E. Gómez, C. Shui Zhang, L. Boratto, M. Salamó, and G. Ramos, “Enabling cross-continent provider fairness in educational recommender systems,” Future Gener. Comput. Syst., vol. 127, pp. 435–447, 2022.
• E. Gómez, L. Boratto, and M. Salamó, “Provider fairness across continents in collaborative recommender systems,” Inf. Process. Manag., vol. 59, no. 1, p. 102719, 2022.
• M. Marras, L. Boratto, G. Ramos, and G. Fenu, “Equality of learning opportunity via individual fairness in personalized recommendations,” International Journal of Artificial Intel ligence in Education, 2021.
• L. Piras, L. Boratto, and G. Ramos, “Evaluating the prediction bias induced by label imbalance in multi-label classification,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 – 5, 2021, ACM, 2021, pp. 3368–3372.
• G. Ramos, L. Boratto, and M. Marras, “Reputation equity in ranking systems,” in CIKM ’21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 – 5, 2021, ACM, 2021, pp. 3378–3382.
• E. Gómez, C. Shui Zhang, L. Boratto, M. Salamó, and M. Marras, “The winner takes it all: Geographic imbalance and provider (un)fairness in educational recommender systems,” in SIGIR ’21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021. ACM, 2021, pp. 1808–1812.
• E. Gómez, L. Boratto, and M. Salamó, “Disparate impact in item recommendation: A case of geographic imbalance,” in Advances in Information Retrieval – 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 – April 1, 2021, Proceedings, Part I, ser. Lecture Notes in Computer Science, vol. 12656. Springer, 2021, pp. 190–206.
• A. Ariza, F. Fabbri, L. Boratto, and M. Salamó, “From the Beatles to Billie Eilish: Connecting provider representativeness and exposure in session-based recommender systems,” in Advances in Information Retrieval – 43rd European Conference on IR Research, ECIR 2021, Virtual Event, March 28 – April 1, 2021, Proceedings, Part II, ser. Lecture Notes in Computer Science, vol. 12657. Springer, 2021, pp. 201–208.
• L. Boratto, G. Fenu, and M. Marras, “Connecting user and item perspectives in popularity debiasing for collaborative recommendation,” Inf. Process. Manag., vol. 58, no. 1, p. 102387, 2021.
• G. Ramos and L. Boratto, “Reputation (in)dependence in ranking systems: Demographics influence over output disparities,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020 ACM, 2020, pp. 2061–2064.
• F. Fabbri, F. Bonchi, L. Boratto, and C. Castillo, “The effect of homophily on disparate visibility of minorities in people recommender systems,” in Proceedings of the Fourteenth International AAAI Conference on Web and Social Media, ICWSM 2020, AAAI Press, 2020, pp. 165–175.
• L. Boratto, G. Fenu, and M. Marras, “The effect of algorithmic bias on recommender systems for massive open online courses,” in Advances in Information Retrieval – 41st European Conference on IR Research, ECIR 2019, Proceedings, Part I, ser. Lecture Notes in Computer Science, vol. 11437. Springer, 2019, pp. 457–472.

Group recommendation

• J.D. Silveira, M. Salamò, L. Boratto: “Enabling reproducibility in group recommender systems”, in Artificial Intelligence Research and Development – Proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2022, Sitges, Spain, 19-21 October 2022, Frontiers in Artificial Intelligence and Applications, vol. 356, pp. 115-124. IOS Press (2022).
• D. Contreras, M. Salamó, and L. Boratto, “Integrating collaboration and leadership in conversational group recommender systems,” ACM Trans. Inf. Syst., vol. 39, no. 4, pp. 41:1–41:32, 2021.
• L. Recalde, J. Mendieta, L. Boratto, L. Terán, C. Vaca, and G. Baquerizo, “Who you should not follow: Extracting word embeddings from tweets to identify groups of interest and hijackers in demonstrations,” IEEE Trans. Emerging Topics Comput., vol. 7, no. 2, pp. 206–217, 2019.
• L. Boratto, S. Carta, and G. Fenu, “Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios,” Information Sciences, vol. 378, pp. 424–443, 2017.
• L. Recalde, D.F. Nettleton, R. Baeza-Yates, and L. Boratto, “Detection of Trending Topic Communities: Bridging Content Creators and Distributors,” in Proceedings of the 28th ACM Conference on Hypertext and Social Media (Hypertext 2017), 2017.
• L. Boratto, S. Carta, G. Fenu, F. Mulas, and P. Pilloni, “Influence of rating prediction on group recommendation’s accuracy,” IEEE Intelligent Systems, vol. 31, no. 6, pp. 22–27, 2016.
• L. Boratto, S. Carta, and G. Fenu, “Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering,” Future Generation Computer Systems, vol. 64, pp. 165–174, 2016.
• L. Boratto, S. Carta, and G. Fenu, “Analysis of the properties that affect the accuracy of a group recommender system,” in 2016 Global Summit on Computer Information Technology (GSCIT), July 2016, pp. 102–107.
• L. Boratto and S. Carta, “ART: group recommendation approaches for automatically detected groups,” International Journal of Machine Learning & Cybernetics, vol. 6, no. 6, pp. 953–980, 2015.
• L. Boratto and S. Carta, “The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation,” Journal of Intelligent Information Systems, vol. 45, no. 2, pp. 221–245, 2015.
• L. Boratto, G. Fenu, and P. L. Pau, “Design criteria to model groups in big data scenarios: Algorithms and best practices,” in 1st International Workshop on Knowledge Discovery on the WEB (KDWEB 2015), ser. CEUR Workshop Proceedings, vol. 1489, 2015, pp. 8–16.
• L. Boratto and S. Carta, “Impact of content novelty on the accuracy of a group recommender system,” in Data Warehousing and Knowledge Discovery – 16th International Conference, DaWaK 2014, 2014. Proceedings, ser. Lecture Notes in Computer Science, vol. 8646. Springer, 2014, pp. 159–170.
• L. Boratto and S. Carta, “Modeling the preferences of a group of users detected by clustering: a group recommendation case-study,” in 4th International Conference on Web Intelligence, Mining and Semantics (WIMS 14), WIMS ’14. ACM, 2014, pp. 16:1–16:7.
• L. Boratto and S. Carta, “Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system,” in ICEIS 2014 – Proceedings of the 16th International Conference on Enterprise Information Systems, Volume 2. SciTePress, 2014, pp. 564–572.
• L. Boratto and S. Carta, “Exploring the ratings prediction task in a group recommender system that automatically detects groups,” in IMMM 2013, The Third International Conference on Advances in Information Mining and Management, 2013, pp. 36–43.
• L. Boratto and S. Carta, “State-of-the-art in group recommendation and new approaches for automatic identification of groups,” in Information Retrieval and Mining in Distributed Environments, ser. Studies in Computational Intelligence. Springer Berlin Heidelberg, 2011, vol. 324, pp. 1–20.
• L. Boratto, S. Carta, and M. Satta, “Groups identification and individual recommendations in group recommendation algorithms,” in Proceedings of the Workshop on Practical Use of Recommender Systems, Algorithms and Technologies 2010, ser. CEUR Workshop Proceedings, vol. 676, November 2010, pp. 27–34.
• L. Boratto, S. Carta, A. Chessa, M. Agelli, and M. L. Clemente, “Group recommendation with automatic identification of users communities,” in Proceedings of the 2009 IEEE/WIC/ACM International Conference on Web Intelligence and International Conference on Intelligent Agent Technology – Workshops. IEEE Computer Society, 2009, pp. 547–550.

User engagement and personalization in eCoaching platforms

• L. Boratto, S. Carta, F. Ibba, F. Mulas, and P. Pilloni, “Modeling real-time data and contextual information from workouts in ecoaching platforms to predict users’ sharing behavior on facebook,” User Modeling and User-Adapted Interaction, Mar 2019.
• P. Pilloni, L. Piras, S. Carta, G. Fenu, F. Mulas, and L. Boratto, “Recommender system lets coaches identify and help athletes who begin losing motivation,” IEEE Computer, vol. 51, no. 3, pp. 36–42, 2018.
• L. Boratto, S. Carta, W. Iguider, F. Mulas, and P. Pilloni, “Predicting workout quality to help coaches support sportspeople,” in Proceedings of the 3rd International Workshop on Health Recommender Systems, HealthRecSys 2018, co-located with the 12th ACM Conference on Recommender Systems (ACM RecSys 2018), ser. CEUR Workshop Proceedings, vol. 2216. CEUR-WS.org, 2018, pp. 8–12.
• L. Boratto, S. Carta, F. Mulas, and P. Pilloni, “An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes,” Personal and Ubiquitous Computing, vol. 21, no. 4, pp. 689–704, 2017.
• L. Boratto, S. Carta, G. Fenu, M. Manca, F. Mulas, and P. Pilloni, “The role of social interaction on users motivation to exercise: A persuasive web framework to enhance the self-management of a healthy lifestyle,” Pervasive and Mobile Computing, vol. 36, pp. 98–114, 2017.
• P. Pilloni, L. Piras, L. Boratto, S. Carta, G. Fenu, and F. Mulas, “Recommendation in persuasive ehealth systems: an effective strategy to spot users’ losing motivation to exercise,” in Proceedings of the 2nd International Workshop on Health Recommender Systems co-located with the 11th International Conference on Recommender Systems (RecSys 2017), ser. CEUR Workshop Proceedings, vol. 1953. CEUR-WS.org, 2017, pp. 6–9.
• L. Balvis, L. Boratto, F. Mulas, L. D. Spano, S. Carta, and G. Fenu, “Keep the beat: Audio guidance for runner training,” in Human-Centered and Error-Resilient Systems Development 2016 ser. Lecture Notes in Computer Science, vol. 9856. Springer, 2016, pp. 246–257.
• F. Mulas, P. Pilloni, M. Manca, L. Boratto, and S. Carta, “Linking human-computer interaction with the social web: A web application to improve motivation in the exercising activity of users,” in Cognitive Infocommunications (CogInfoCom), 2013 IEEE 4th International Conference on, December 2013, pp. 351–356.
• F. Mulas, P. Pilloni, M. Manca, L. Boratto, and S. Carta, “Using new communication technologies and social media interaction to improve the motivation of users to exercise,” in Future Generation Communication Technology (FGCT), 2013 Second International Conference on, November 2013, pp. 87–92.

User modeling and personalization for mobility

• C. Consonni, S. Basile, M. Manca, L. Boratto, A. Freitas, T. Kovacikova, G. Pourhashem, and Y. Cornet, “What’s your value of travel time? collecting traveler-centered mobility data via crowdsourcing,” in Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, ICWSM 2021, held virtual ly, June 7-10, 2021. AAAI Press, 2021, pp. 961–970.
• M. Manca, L. Boratto, and S. Carta, “Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system,” Information Systems Frontiers, vol. 20, no. 4, pp. 825–839, 2018.
• M. Manca, L. Boratto, and S. Carta, “Using behavioral data mining to produce friend recommendations in a social bookmarking system,” in Data Management Technologies and Applications: Third International Conference, DATA 2014, Revised Selected papers. Springer International Publishing, 2015, pp. 99–116.
• M. Manca, L. Boratto, and S. Carta, “Friend recommendation in a social bookmarking system: Design and architecture guidelines,” in Intelligent Systems in Science and Information 2014, ser. Studies in Computational Intelligence. Springer International Publishing, 2015, vol. 591, pp. 227–242.
• M. Manca, L. Boratto, and S. Carta, “Mining user behavior in a social bookmarking system – A delicious friend recommender system,” in DATA 2014 – Proceedings of 3rd International Conference on Data Management Technologies and Applications. SciTePress, 2014, pp. 331–338.
• M. Manca, L. Boratto, and S. Carta, “Design and architecture of a friend recommender system in the social bookmarking domain,” in Science and Information Conference (SAI), 2014, Aug 2014, pp. 838–842.
• M. Manca, L. Boratto, and S. Carta, “Producing friend recommendations in a social bookmarking system by mining users content,” in The Third International Conference on Advances in Information Mining and Management (IMMM 2013), 2013, pp. 59–64.

Semantics-aware user targeting, ranking, and recommendation

• F. M. Malloci, L. Portell Penadés, L. Boratto, and G. Fenu, “A text mining approach to extract and rank innovation insights from research projects,” in Web Information Systems Engineering – WISE 2020 – 21st International Conference, Proceedings, Part II, ser. Lecture Notes in Computer Science, vol. 12343. Springer, 2020, pp. 143–154.
• L. Boratto, S. Carta, G. Fenu, and L. Piras, “Employing document embeddings to sove the “new catalog” problem in user targeting, and provide explanations to the users,” in Advances in Information Retrieval – 40th European Conference on IR Research, ECIR 2018, Proceedings. Springer, 2018, pp. 371–382.
• L. Boratto, S. Carta, G. Fenu, and R. Saia, “Semantics-aware content-based recommender systems: Design and architecture guidelines,” Neurocomputing, vol. 254, pp. 79–85, 2017.
• R. Saia, L. Boratto, and S. Carta, “A Semantic Approach to Remove Incoherent Items From a User Profile and Improve the Accuracy of a Recommender System,” Journal of Intelligent Information Systems Springer, 2016.
• L. Boratto, S. Carta, G. Fenu, and R. Saia, “Using neural word embeddings to model user behavior and detect user segments,” Knowledge-Based Systems, vol. 108, pp. 5–14, 2016.
• R. Saia, L. Boratto, S. Carta, and G. Fenu, “Binary sieves: Toward a semantic approach to user segmentation for behavioral targeting,” Future Generation Computer Systems, vol. 64, pp. 186–197, 2016.
• L. Boratto, S. Carta, G. Fenu, and R. Saia, “Representing items as word-embedding vectors and generating recommendations by measuring their linear independence,” in Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), ser. CEUR Workshop Proceedings, vol. 1688. CEUR-WS.org, 2016.
• R. Saia, L. Boratto, and S. Carta, “A class-based strategy to user behavior modeling in recommender systems,” in Emerging Trends and Advanced Technologies for Computational Intelligence: Extended and Selected Results from the Science and Information Conference 2015. Springer International Publishing, 2016, pp. 241–260.
• L. Boratto, S. Carta, G. Fenu, and R. Saia, “Exploiting a determinant-based metric to evaluate a word-embeddings matrix of items,” in IEEE International Conference on Data Mining Workshops, ICDM Workshops 2016.. IEEE, 2016, pp. 984–991.
• R. Saia, L. Boratto, and S. Carta, “A latent semantic pattern recognition strategy for an untrivial targeted advertising,” in Proceedings of the 2015 IEEE International Congress on Big Data. IEEE Computer Society, 2015, pp. 491–498.
• R. Saia, L. Boratto, and S. Carta, “Semantic coherence-based user profile modeling in the recommender systems context,” in KDIR 2014 – Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. SciTePress, 2014, pp. 154–161.

Other

• M. Güell, M. Salamó, D. Contreras, and L. Boratto, “Integrating a cognitive assistant within a critique-based recommender system,” Cogn. Syst. Res., vol. 64, pp. 1–14, 2020.
• G. Fenu, M. Marras, and L. Boratto, “A multi-biometric system for continuous student authentication in e-learning platforms,” Pattern Recognition Letters, vol. 113, pp. 83–92, 2018.
• G. Armano, S. Battiato, D. Bennato, L. Boratto, S. M. Carta, T. D. Noia, E. D. Sciascio, A. Ortis, and D. R. Recupero, “Newsvallum: Semantics-aware text and image processing for fake news detection system,” in Proceedings of the 26th Italian Symposium on Advanced Database Systems, Castellaneta Marina (Taranto), Italy, June 24-27, 2018, ser. CEUR Workshop Proceedings, vol. 2161. CEUR-WS.org, 2018.
• L. Boratto, S. Carta, and R. Saia, “Improving the accuracy of latent-space-based recommender systems by introducing a cut-off criterion,” in Proceedings of the Workshop on Engineering Computer-Human Interaction in Recommender Systems., ser. CEUR Workshop Proceedings, vol. 1705. CEUR-WS.org, 2016, pp. 44–53.
• L. Boratto, M. Cadeddu, S. Carta, G. Deplano, and F. Mereu, “A tool to analyze the reading behavior of the users in a mobile digital publishing platform,” in Proceedings of the 2nd International Workshop on Knowledge Discovery on the WEB, KDWeb 2016, Cagliari, Italy, September 8-10, 2016., ser. CEUR Workshop Proceedings, vol. 1748. CEUR-WS.org, 2016.
• R. Saia, L. Boratto, and S. Carta, “Exploiting the evaluation frequency of the items to enhance the recommendation accuracy,” in 2016 Global Summit on Computer Information Technology (GSCIT), July 2016, pp. 108–113.
• R. Saia, L. Boratto, and S. Carta, “Multiple behavioral models: A divide and conquer strategy to fraud detection in financial data streams,” in KDIR 2015 – Proceedings of the International Conference on Knowledge Discovery and Information Retrieval. SciTePress, 2015, pp. 496–503.
• R. Saia, L. Boratto, and S. Carta, “A new perspective on recommender systems: A class path information model,” in 2015 Science and Information Conference (SAI). IEEE, 2015, pp. 578–585.
• L. Boratto, S. Carta, M. Manca, F. Mulas, P. Pilloni, G. Pinna, and E. Vargiu, “A clustering approach for tag recommendation in social environments,” International Journal of E-Business Development, vol. 3, pp. 126–136, 2013.
• L. Boratto, S. Carta, and E. Vargiu, “RATC: A robust automated tag clustering technique,” in E-Commerce and Web Technologies, 10th International Conference, EC-Web 2009, Linz, Austria, September 1-4, 2009. Proceedings, ser. Lecture Notes in Computer Science. Springer, 2009, pp. 324–335.