Publications - Ludovico Boratto

Books and monographs

Advances in Bias and Fairness in Information Retrieval: 5th International Workshop, BIAS 2024, with Alejandro Bellogin, Ludovico Boratto, Styliani Kleanthous, Elisabeth Lex, Francesca Maridina Malloci, Mirko Marras (Springer, 2024, 170 pages)
Advances on Graph-Based Approaches in Information Retrieval: First International Workshop, IRonGraphs 2024, with Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato (Springer, 2024, 170 pages)
Group Recommender Systems: An Introduction (Second Edition), with Alexander Felfernig, Martin Stettinger, Marko Tkalčič (Springer, 2024, 192 pages)
Advances in Bias and Fairness in Information Retrieval, Fourth International Workshop, BIAS 2023, with Stefano Faralli, Mirko Marras, Giovanni Stilo (Springer, 2023, 176 pages)
Advances in Bias and Fairness in Information Retrieval, Third International Workshop, BIAS 2022, with Stefano Faralli, Mirko Marras, Giovanni Stilo (Springer, 2022, 155 pages)
Advances in Bias and Fairness in Information Retrieval, Second International Workshop, BIAS 2021, with Stefano Faralli, Mirko Marras, Giovanni Stilo (Springer, 2021, 171 pages)
Bias and Social Aspects in Search and Recommendation, with Stefano Faralli, Mirko Marras, Giovanni Stilo (Springer, 2020, 205 pages)
Group Recommender Systems: An Introduction, with Alexander Felfernig, Martin Stettinger, Marko Tkalčič (Springer, 2018, 176 pages)

Papers by topic

Explainable recommendation

Balloccu, G., Boratto, L., Fenu, G., Marras, M., & Soccol, A. (2024, October). KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in Recommendation. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1079-1084).
Ariza-Casabona, A., Boratto, L., & Salamó, M. (2024, October). A Comparative Analysis of Text-Based Explainable Recommender Systems. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 105-115).
Ariza-Casabona, A., Salamó, M., Boratto, L., & Fenu, G. (2023, September). Towards Self-Explaining Sequence-Aware Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (pp. 904-911).
Balloccu, G., Boratto, L., Cancedda, C., Fenu, G., & Marras, M. (2023, March). Knowledge is power, understanding is impact: Utility and beyond goals, explanation quality, and fairness in path reasoning recommendation. In European Conference on Information Retrieval (pp. 3-19). Cham: Springer Nature Switzerland.
Balloccu, G., Boratto, L., Fenu, G., & Marras, M. (2023). Reinforcement recommendation reasoning through knowledge graphs for explanation path quality. Knowledge-Based Systems, 260, 110098.
Balloccu, G., Boratto, L., Fenu, G., & Marras, M. (2022). XRecSys: A framework for path reasoning quality in explainable recommendation. Software Impacts, 14, 100404.
Balloccu, G., Boratto, L., Fenu, G., & Marras, M. (2022, July). Post processing recommender systems with knowledge graphs for recency, popularity, and diversity of explanations. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 646-656).

Beyond-accuracy perspectives in recommendation

Gómez, E., Contreras, D., Boratto, L., & Salamo, M. (2024, October). AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 137-147).
Boratto, L., Fabbri, F., Fenu, G., Marras, M., & Medda, G. (2024, October). Fair Augmentation for Graph Collaborative Filtering. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 158-168).
Martinez, A., Tufis, M., & Boratto, L. (2024, July). Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative Filtering. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1753-1762).
Gómez, E., Contreras, D., Salamo, M., & Boratto, L. (2024, June). Bringing Equity to Coarse and Fine-Grained Provider Groups in Recommender Systems. In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 18-23).
Dokoupil, P., Boratto, L., & Peska, L. (2024, June). User perceptions of diversity in recommender systems. In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 212-222).
Boratto, L., Fabbri, F., Fenu, G., Marras, M., & Medda, G. (2024, March). Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation. In European Conference on Information Retrieval (pp. 38-55). Cham: Springer Nature Switzerland.
Boratto, L., Cerniglia, G., Marras, M., Perniciano, A., & Pes, B. (2024, March). A Cost-Sensitive Meta-learning Strategy for Fair Provider Exposure in Recommendation. In European Conference on Information Retrieval (pp. 440-448). Cham: Springer Nature Switzerland.
Sánchez, P., Bellogín, A., & Boratto, L. (2023). Bias characterization, assessment, and mitigation in location-based recommender systems. Data Mining and Knowledge Discovery, 37(5), 1885-1929.
Boratto, L., Fabbri, F., Fenu, G., Marras, M., & Medda, G. (2023, October). Counterfactual graph augmentation for consumer unfairness mitigation in recommender systems. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 3753-3757).
Dokoupil, P., Peska, L., & Boratto, L. (2023, July). 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 (pp. 2354-2358).
Medda, G., Fabbri, F., Marras, M., Boratto, L., & Fenu, G. (2023). GNNUERS: Fairness explanation in GNNs for recommendation via counterfactual reasoning. ACM Transactions on Intelligent Systems and Technology.
Boratto, L., Fenu, G., Marras, M., & Medda, G. (2023). Practical perspectives of consumer fairness in recommendation. Information Processing & Management, 60(2), 103208.
Marras, M., Boratto, L., Ramos, G., & Fenu, G. (2022). Equality of learning opportunity via individual fairness in personalized recommendations. International Journal of Artificial Intelligence in Education, 32(3), 636-684.
Marras, M., Boratto, L., Ramos, G., & Fenu, G. (2022, July). Regulating group exposure for item providers in recommendation. In Proceedings of the 45th International ACM SIGIR Conference on research and development in information retrieval (pp. 1839-1843).
Boratto, L., Fenu, G., Marras, M., & Medda, G. (2022, April). Consumer fairness in recommender systems: Contextualizing definitions and mitigations. In European Conference on Information Retrieval (pp. 552-566). Cham: Springer International Publishing.
Gómez, E., Zhang, C. S., Boratto, L., Salamó, M., & Ramos, G. (2022). Enabling cross-continent provider fairness in educational recommender systems. Future Generation Computer Systems, 127, 435-447.
Gómez, E., Boratto, L., & Salamó, M. (2022). Provider fairness across continents in collaborative recommender systems. Information Processing & Management, 59(1), 102719.
Gómez, E., Shui Zhang, C., Boratto, L., Salamó, M., & Marras, M. (2021, July). The winner takes it all: geographic imbalance and provider (un)fairness in educational recommender systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1808-1812).
Boratto, L., Fenu, G., & Marras, M. (2021). Interplay between upsampling and regularization for provider fairness in recommender systems. User Modeling and User-Adapted Interaction, 31(3), 421-455.
Ariza, A., Fabbri, F., Boratto, L., & Salamó, M. (2021, March). From The Beatles to Billie Eilish: connecting provider representativeness and exposure in session-based recommender systems. In European Conference on Information Retrieval (pp. 201-208). Cham: Springer International Publishing.
Gómez, E., Boratto, L., & Salamó, M. (2021, March). Disparate impact in item recommendation: A case of geographic imbalance. In European Conference on Information Retrieval (pp. 190-206). Cham: Springer International Publishing.
Boratto, L., Fenu, G., & Marras, M. (2021). Connecting user and item perspectives in popularity debiasing for collaborative recommendation. Information Processing & Management, 58(1), 102387.
Fabbri, F., Bonchi, F., Boratto, L., & Castillo, C. (2020, May). The effect of homophily on disparate visibility of minorities in people recommender systems. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 14, pp. 165-175).
Ramos, G., Boratto, L., & Caleiro, C. (2020). On the negative impact of social influence in recommender systems: A study of bribery in collaborative hybrid algorithms. Information Processing & Management, 57(2), 102058.
Boratto, L., Fenu, G., & Marras, M. (2019, April). The effect of algorithmic bias on recommender systems for massive open online courses. In European conference on information retrieval (pp. 457-472). Cham: Springer International Publishing.

Beyond-accuracy perspectives in non-personalized ranking

Ramos, G., Boratto, L., & Marras, M. (2024). Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation. IEEE Transactions on Big Data, (01), 1-8.
Ramos, G., Marras, M., & Boratto, L. (2024, July). Towards Ethical Item Ranking: A Paradigm Shift from User-Centric to Item-Centric Approaches. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2667-2671).
Ramos, G., Boratto, L., & Marras, M. (2022). Robust reputation independence in ranking systems for multiple sensitive attributes. Machine Learning, 111(10), 3769-3796.
Ramos, G., Boratto, L., & Marras, M. (2021, October). Reputation equity in ranking systems. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 3378-3382).
Saúde, J., Ramos, G., Boratto, L., & Caleiro, C. (2021). A robust reputation-based group ranking system and its resistance to bribery. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(2), 1-35.
Ramos, G., & Boratto, L. (2020, July). 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 (pp. 2061-2064).

Beyond-accuracy perspectives in classification

Purificato, E., Boratto, L., & De Luca, E. W. (2024). Toward a Responsible Fairness Analysis: From Binary to Multiclass and Multigroup Assessment in Graph Neural Network-Based User Modeling Tasks. Minds and Machines, 34(3), 33.
Abdelrazek, M., Purificato, E., Boratto, L., & De Luca, E. W. (2023, July). 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 (pp. 3165-3169).
Purificato, E., Boratto, L., & De Luca, E. W. (2022, October). 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 (pp. 4399-4403).
Piras, L., Boratto, L., & Ramos, G. (2021, October). Evaluating the Prediction Bias Induced by Label Imbalance in Multi-label Classification. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 3368-3372).

Multi-objective recommendation

Dokoupil, P., Peska, L., & Boratto, L. (2024, July). SM-RS: Single-and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 988-995).
Gómez, E., Contreras, D., Boratto, L., & Salamó, M. (2024, March). MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels. In European Conference on Information Retrieval (pp. 21-38). Cham: Springer Nature Switzerland.
Paparella, V., Anelli, V. W., Boratto, L., & Di Noia, T. (2023, September). Reproducibility of multi-objective reinforcement learning recommendation: Interplay between effectiveness and beyond-accuracy perspectives. In Proceedings of the 17th ACM Conference on Recommender Systems (pp. 467-478).
Dokoupil, P., Peska, L., & Boratto, L. (2023, September). 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 (pp. 912-918).

Knowledge Graph Construction and Exploration

Afreen, N., Balloccu, G., Boratto, L., Fenu, G., Malloci, F. M., Marras, M., & Martis, A. G. (2024, October). EDGE: A Conversational Interface driven by Large Language Models for Educational Knowledge Graphs Exploration. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 5159-5163).
Afreen, N., Balloccu, G., Boratto, L., Fenu, G., Malloci, F. M., Marras, M., & Martis, A. G. (2024, June). Learner-centered Ontology for Explainable Educational Recommendation. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 567-575).

Group recommendation

Silveira, J. D., Salamó, M., & Boratto, L. (2022). Enabling Reproducibility in Group Recommender Systems. In Artificial Intelligence Research and Development (pp. 115-124). IOS press.
Contreras, D., Salamó, M., & Boratto, L. (2021). Integrating collaboration and leadership in conversational group recommender systems. ACM Transactions on Information Systems (TOIS), 39(4), 1-32.
Boratto, L., Carta, S., & Fenu, G. (2017). Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios. Information Sciences, 378, 424-443.
Boratto, L., Carta, S., Fenu, G., Mulas, F., & Pilloni, P. (2016). Influence of rating prediction on group recommendation’s accuracy. IEEE Intelligent Systems, 31(6), 22-27.
Boratto, L., Carta, S., & Fenu, G. (2016). Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering. Future Generation Computer Systems, 64, 165-174.
Boratto, L., Carta, S., & Fenu, G. (2016, July). Analysis of the properties that affect the accuracy of a group recommender system. In 2016 Global Summit on Computer & Information Technology (GSCIT) (pp. 102-107). IEEE.
Boratto, L., & Carta, S. (2015). ART: group recommendation approaches for automatically detected groups. International Journal of Machine Learning and Cybernetics, 6(6), 953-980.
Boratto, L., & Carta, S. (2015). The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation. Journal of Intelligent Information Systems, 45(2), 221-245.
Boratto, L., & Carta, S. (2014, June). Modeling the preferences of a group of users detected by clustering: A group recommendation case-study. In Proceedings of the 4th international conference on web intelligence, mining and semantics (WIMS14) (pp. 1-7).
Boratto, L., & Carta, S. (2014, April). Using collaborative filtering to overcome the curse of dimensionality when clustering users in a group recommender system. In International Conference on Enterprise Information Systems (Vol. 2, pp. 564-572). SCITEPRESS.
Boratto, L., & Carta, S. (2014). Impact of content novelty on the accuracy of a group recommender system. In Data Warehousing and Knowledge Discovery: 16th International Conference, DaWaK 2014, Munich, Germany, September 2-4, 2014. Proceedings 16 (pp. 159-170). Springer International Publishing.
Boratto, L., & Carta, S. (2011). State-of-the-art in group recommendation and new approaches for automatic identification of groups. In Information retrieval and mining in distributed environments (pp. 1-20). Berlin, Heidelberg: Springer Berlin Heidelberg.
Boratto, L., Carta, S., & Satta, M. (2010, September). Groups Identification and Individual Recommendations in Group Recommendation Algorithms. In PRSAT@ recsys (pp. 27-34).
Boratto, L., Carta, S., Chessa, A., Agelli, M., & Clemente, M. L. (2009, September). Group recommendation with automatic identification of users communities. In 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (Vol. 3, pp. 547-550). IEEE.

User engagement and personalization in eCoaching platforms

Boratto, L., Carta, S., Iguider, W., Mulas, F., & Pilloni, P. (2022). Fair performance-based user recommendation in eCoaching systems. User Modeling and User-Adapted Interaction, 32(5), 839-881.
Boratto, L., Carta, S., Ibba, F., Mulas, F., & Pilloni, P. (2020). 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, 30(3), 395-411.
Pilloni, P., Piras, L., Carta, S., Fenu, G., Mulas, F., & Boratto, L. (2018). Recommender system lets coaches identify and help athletes who begin losing motivation. Computer, 51(3), 36-42.
Boratto, L., Carta, S., Iguider, W., Mulas, F., & Pilloni, P. (2018). Predicting workout quality to help coaches support sportspeople. In CEUR Workshop Proceedings (Vol. 2216, pp. 8-12). CEUR-WS.
Boratto, L., Carta, S., Mulas, F., & Pilloni, P. (2017). An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes. Personal and Ubiquitous Computing, 21, 689-704.
Boratto, L., Carta, S., Fenu, G., Manca, M., Mulas, F., & Pilloni, P. (2017). 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, 36, 98-114.
Pilloni, P., Piras, L., Boratto, L., Carta, S., Fenu, G., & Mulas, F. (2017). Recommendation in persuasive eHealth systems: An effective strategy to spot users’ losing motivation to exercise. In CEUR Workshop Proceedings (Vol. 1953, pp. 6-9).
Balvis, L., Boratto, L., Mulas, F., Spano, L. D., Carta, S., & Fenu, G. (2016). Keep the beat: Audio guidance for runner training. In Human-Centered and Error-Resilient Systems Development: IFIP WG 13.2/13.5 Joint Working Conference, 6th International Conference on Human-Centered Software Engineering, HCSE 2016, and 8th International Conference on Human Error, Safety, and System Development, HESSD 2016, Stockholm, Sweden, August 29-31, 2016, Proceedings 8 (pp. 246-257). Springer International Publishing.
Mulas, F., Pilloni, P., Manca, M., Boratto, L., & Carta, S. (2013, December). Linking Human-Computer Interaction with the Social Web: A web application to improve motivation in the exercising activity of users. In 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom) (pp. 351-356). IEEE.
Mulas, F., Pilloni, P., Manca, M., Boratto, L., & Carta, S. (2013, November). Using new communication technologies and social media interaction to improve the motivation of users to exercise. In Second International Conference on Future Generation Communication Technologies (FGCT 2013) (pp. 87-92). IEEE.
Boratto, L., & Carta, S. (2013). 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 (pp. 36-43).

User modeling and personalization for mobility

Consonni, C., Basile, S., Manca, M., Boratto, L., Freitas, A., Kovacikova, T., Pourhashem, G. and Cornet, Y. (2021, May). What’s Your Value of Travel Time? Collecting Traveler-Centered Mobility Data via Crowdsourcing. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 15, pp. 961-970).
Basile, S., Consonni, C., Manca, M., & Boratto, L. (2020, July). Matching user preferences and behavior for mobility. In Proceedings of the 31st ACM Conference on Hypertext and Social Media (pp. 141-150).
Boratto, L., Manca, M., Lugano, G., & Gogola, M. (2020). Characterizing user behavior in journey planning. Computing, 102(5), 1245-1258.
Manca, M., Boratto, L., Roman, V. M., i Gallissà, O. M., & Kaltenbrunner, A. (2017). Using social media to characterize urban mobility patterns: State-of-the-art survey and case-study. Online social networks and media, 1, 56-69.

Social recommendation

Manca, M., Boratto, L., & Carta, S. (2018). Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system. Information Systems Frontiers, 20, 825-839.
Manca, M., Boratto, L., & Carta, S. (2015). Using behavioral data mining to produce friend recommendations in a social bookmarking system. In Data Management Technologies and Applications: Third International Conference, DATA 2014, Vienna, Austria, August 29-31, 2014, Revised Selected papers 3 (pp. 99-116). Springer International Publishing.
Manca, M., Boratto, L., & Carta, S. (2015). Friend recommendation in a social bookmarking system: Design and architecture guidelines. In Intelligent Systems in Science and Information 2014: Extended and Selected Results from the Science and Information Conference 2014 (pp. 227-242). Springer International Publishing.
Manca, M., Boratto, L., & Carta, S. (2014, August). Design and architecture of a friend recommender system in the social bookmarking domain. In 2014 Science and Information Conference (pp. 838-842). IEEE.
Manca, M., Boratto, L., & Carta, S. (2014, August). Mining user behavior in a social bookmarking system-A delicious friend recommender system. In International Conference on Data Management Technologies and Applications (Vol. 2, pp. 331-338). SCITEPRESS.
Manca, M., Boratto, L., & Carta, S. (2013). Producing friend recommendations in a social bookmarking system by mining users content. In International Conference on Advances in Information Mining and Management (IMMM’13).

Semantics-aware user targeting, ranking, and recommendation

Boratto, L., Carta, S., Fenu, G., & Saia, R. (2017). Semantics-aware content-based recommender systems: Design and architecture guidelines. Neurocomputing, 254, 79-85.
Saia, R., Boratto, L., Carta, S., & Fenu, G. (2016). Binary sieves: Toward a semantic approach to user segmentation for behavioral targeting. Future Generation Computer Systems, 64, 186-197.
Boratto, L., Carta, S., Fenu, G., & Saia, R. (2016). Using neural word embeddings to model user behavior and detect user segments. Knowledge-based systems, 108, 5-14.
Saia, R., Boratto, L., & Carta, S. (2016). A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system. Journal of Intelligent Information Systems, 47, 111-134.
Saia, R., Boratto, L., & Carta, S. (2016). Introducing a Weighted Ontology to Improve the Graph-based Semantic Similarity Measures. INTERNATIONAL JOURNAL OF SIGNAL PROCESSING SYSTEMS, 4(5), 375-381.
Boratto, L., Carta, S., Fenu, G., & Saia, R. (2016, December). Exploiting a Determinant-based Metric to Evaluate a Word-embeddings Matrix of Items. In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (pp. 984-991). IEEE.
Saia, R., Boratto, L., & Carta, S. (2016). 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 (pp. 241-260). Springer International Publishing.
Saia, R., Boratto, L., & Carta, S. (2016, July). Exploiting the evaluation frequency of the items to enhance the recommendation accuracy. In 2016 Global Summit on Computer & Information Technology (GSCIT) (pp. 108-113). IEEE.
Saia, R., Boratto, L., & Carta, S. (2015, July). A new perspective on recommender systems: A class path information model. In 2015 Science and Information Conference (SAI) (pp. 578-585). IEEE.
Saia, R., Boratto, L., & Carta, S. (2015, June). A latent semantic pattern recognition strategy for an untrivial targeted advertising. In 2015 IEEE international congress on big data (pp. 491-498). IEEE.
Saia, R., Boratto, L., & Carta, S. (2014, October). Semantic coherence-based user profile modeling in the recommender systems context. In International Conference on Knowledge Discovery and Information Retrieval (Vol. 2, pp. 154-161). SciTePress.

Other recommendation tasks

Ferrara, A., Valentini, M., Masciullo, P., De Candia, A., Abbattista, D., Fusco, R., Pomo, C., Anelli, V.W., Biancofiore, G.M., Boratto, L. and Narducci, F. (2024). DIVAN: Deep-Interest Virality-Aware Network to Exploit Temporal Dynamics in News Recommendation. In Proceedings of the Recommender Systems Challenge 2024 (pp. 12-16).
Güell, M., Salamó, M., Contreras, D., & Boratto, L. (2020). Integrating a cognitive assistant within a critique-based recommender system. Cognitive Systems Research, 64, 1-14.
Jiménez, O., Salamó, M., & Boratto, L. (2019). Distance-aware event recommendation in event-based social networks. In Artificial Intelligence Research and Development (pp. 235-244). IOS Press.
Boratto, L., Carta, S., Fenu, G., & Piras, L. (2018). Employing Document Embeddings to Solve 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, Grenoble, France, March 26-29, 2018, Proceedings 40 (pp. 371-382). Springer International Publishing.
Atzori, M., Boratto, L., & Spano, L. D. (2017, June). Towards Chatbots as Recommendation Interfaces. In EnCHIReS@ EICS (pp. 26-31).
Recalde, L., Mendieta, J., Boratto, L., Terán, L., Vaca, C., & Baquerizo, G. (2017). Who you should not follow: Extracting word embeddings from tweets to identify groups of interest and hijackers in demonstrations. IEEE Transactions on Emerging Topics in Computing, 7(2), 206-217.
Boratto, L., Carta, S., Fenu, G., & Saia, R. (2016). Representing Items as Word-Embedding Vectors and Generating Recommendations by Measuring their Linear Independence. RecSys Posters, 140.

Other tasks

Tufiş, M., & Boratto, L. (2021). Toward a complete data valuation process. challenges of personal data. Journal of Data and Information Quality (JDIQ), 13(4), 1-7.
Malloci, F. M., Penadés, L. P., Boratto, L., & Fenu, G. (2020, October). A text mining approach to extract and rank innovation insights from research projects. In International Conference on Web Information Systems Engineering (pp. 143-154). Cham: Springer International Publishing.
Fenu, G., Marras, M., & Boratto, L. (2018). A multi-biometric system for continuous student authentication in e-learning platforms. Pattern Recognition Letters, 113, 83-92.
Armano, G., Battiato, S., Bennato, D., Boratto, L., Carta, S.M., Di Noia T., Di Sciascio E., Ortis, A., Reforgiato Recupero D. (2018). NewsVallum: Semantics-Aware Text and Image Processing for Fake News Detection system. In SEBD.
Marras, M., Manca, M., Boratto, L., Fenu, G., & Laniado, D. (2018, April). BarcelonaNow: Empowering citizens with interactive dashboards for urban data exploration. In Companion Proceedings of the The Web Conference 2018 (pp. 219-222).
Recalde, L., Nettleton, D. F., Baeza-Yates, R., & Boratto, L. (2017, July). Detection of trending topic communities: Bridging content creators and distributors. In Proceedings of the 28th ACM Conference on Hypertext and Social Media (pp. 205-213).
Boratto, L., Cadeddu, M., Carta, S., Deplano, G., & Mereu, F. (2016). A Tool to Analyze the Reading Behavior of the Users in a Mobile Digital Publishing Platform. In KDWeb.
Saia, R., Boratto, L., & Carta, S. (2015, November). Multiple behavioral models: A divide and conquer strategy to fraud detection in financial data streams. In 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) (Vol. 1, pp. 496-503). IEEE.
Garau, P., Boratto, L., Carta, S., & Fenu, G. (2015). Up School: Introduction of Pervasive Learning Technologies to Enhance Classic Educational Models. Bulletin of the Technical Committee on Learning Technology, 17(3), 10-13.
Boratto, L., Carta, S., & Vargiu, E. (2009). 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 10 (pp. 324-335). Springer Berlin Heidelberg.