Curriculum Vitae Cataldo Musto, Ph.D.swap/musto/musto_cv.pdf · data sylos, it is possible to feed content-based adaptive platform with a huge amount of freely available knowledge,

Curriculum Vitae

Cataldo Musto, Ph.D.

Assistant Professor - Post Doc

Department of Computer Science

Universita degli Studi di Bari “Aldo Moro”

Curriculum Vitae Cataldo Musto, Ph.D.

Indice

1 Personal Info 3

2 Brief CV 42.1 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Schools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.3 Foreign Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

3 Academic Career 4

4 Scientific Activity 54.1 Summary of the Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54.2 Research Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.3 Partnership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

5 Activities for the Scientific Community 85.1 Participation to Conferences and Workshops . . . . . . . . . . . . . . . . . . . . 85.2 Conference Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115.3 Editorial Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125.4 Program Commitees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125.5 Peer Reviewing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135.6 Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

6 Prizes 16

7 Publications 177.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.2 Scientific Impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177.3 List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Updated: 10 gennaio 2017 pag. 2


1 Personal Info

Birthplace and Birthdate: Corato (Bari, Italy), October 8, 1982Nationality: ItalianEducation: Bachelor in Computer Science

Master Degree in Computer SciencePh.D. in Computer Science

Current Position: Research Assistant - Department of Computer Science,Universita degli Studi di Bari ”‘A.Moro”’

Office: Department of Computer ScienceUniversita degli Studi di Bari “Aldo Moro”Via E. Orabona 4 - I-70126 Bari

Contacts: Email: [email protected]: [email protected]: +390805442497Skype: cataldo.musto

Home page: http://www.di.uniba.it/ swap/index.php?n=Membri.CataldoMusto

Languages: Italian (native)English (good)French(basic)



2 Brief CV

2.1 Education

• June 2012: received a Ph.D. in Computer Science on June, 8 at Universita’ degliStudi di Bari. Title of the dissertation: Enhanced Vector Space Models for Content-basedRecommender Systems. Supervisor: Prof. G. Semeraro.

• June 2012: Doctor Europaeus certification, jointly received with Ph.D. on June, 82012.

• April 2008: received a Master Degree on April, 23 at Universita’ degli Studi di Bari.Title of the thesis: ‘Extending a content-based recommendation model through semanticfolksonomies. Supervisor: Prof. G. Semeraro.

• December 2005: received a Bachelor Degree on December, 14 at Universita’ degliStudi di Bari. Title of the thesis: Web navigation through head-controlled input devices:development of a Mozilla Firefox extension to identify relevant components in HTML webpages. Supervisor: Prof. G. Semeraro.

2.2 Schools

• 2010: IEEE SSSC 2010, Summer School on Semantic Computing, Berkeley (California,United States of America) from 25 to 31 July 2010;

• 2009: ACAI 2009, Advanced Course in Artificial Intelligence Belfast (Northern Ireland)from 23 to 29 August 2009);

2.3 Foreign Languages

Fluency in English (written / spoken). Cataldo Musto has participated in several meetings andpresented his own works at international conferences and workshops.

3 Academic Career

1. January 2016 (current): Assistant Professor at University of Bari, Department ofComputer Science. Research Topic: Semantic Holistic User Modeling for PersonalizedAccess to Digital Services and Content

2. April 2014 - January 2016: PostDoc - Research Assistant at University of Bari,Department of Computer Science. Research Topic: Machine Learning techniques forContent-based Context-aware Content-based Recommender Systems

3. April 2012 - April 2014 : PostDoc - Research Assistant at University of Bari,Department of Computer Science. Research Topic: Distributional Models for Content-based Context-aware Content-based Recommender Systems



4. April 2011 - July 2011: Stage at Philips Research Center of Eindhoven (TheNetherlands) in the Human Computer Interaction & Experiences research group, underthe supervision of Eng. Mauro Barbieri. Topic: TV-Show Retrieval and Classificationfor Personalization of Electronic Program Guides”.

5. January 2009 - December 2011 : Ph.D. in Computer Science at University of Bari.Won a Grant for Ph.D. studentship.

6. June 2008 - January 2009: Research contract at University of Bari. Topic: integra-tion of user-generated content in a content-based recommendation model.

7. May 2007 - December 2007: Research contract at University of Bari. Topic: analysisof the techniques for diagnosis and evaluation of QoS (Quality of Services) in AdaptiveSystems.

Summary:

• n.1 assistant professor position with italian university

• n.2 post-doc with italian university

• n.1 Ph.D. with a research grant;

• n.1 stage with foreign research center;

• n.2 contracts with italian universities

4 Scientific Activity

4.1 Summary of the Research

The research carried out during the Ph.D. and during the PostDoc focused on the developmentof novel methodologies to introduce semantics in content-based representation employed byintelligent and adaptive platforms.

During the Ph.D., this led to the development of a framework, called eVSM (enhanced Vec-tor Space Model), that exploits the typical strengths of Vector Space Model (VSM) and extendsit through a Quantum Negation operator as well as through Distributional Semantics Models(DSM) to semantically represented the information about items and users. DSM implementnon-supervised approaches to represent terms (and documents as well) in large vector spacesaccording to their co-occurrences in large corpora of data. The main advantage that followsthe adoption of DSM is that the semantics conveyed by terms is learned in an incremental way,without any training.

In the experimental sessions the effectiveness of eVSM is evaluated in both online andonline settings, and it emerged that eVSM overcomes several state-of-the-art models in terms ofgoodness of recommendations and accuracy of the proposed ranking. Specifically, it outperformsin a significant way LSI, the classical VSM and a Bayes text classifier in the task of providingusers with recommendations about movies. The effectiveness of the approach was confirmed



during the stage carried out in Philips Research, where the above described techniques wereused to recommend personalized TV shows.

During the PostDoc the research has evolved by following two research lines: on one side, eV-SM model was extended by modeling contextual information as well. This led to the definitionof a context-aware recommendation framework called Contextual eVSM which was evaluated,with good results, against several state-of-the-art baselines. Next, the research focused on theanalysis of semantics-aware representation different from those based on DSM.

As an example, very promising results were obtained by analyzing the effectiveness of theinformation gathered from the Linked Open Data (LOD) cloud. Thanks to these RDF-baseddata sylos, it is possible to feed content-based adaptive platform with a huge amount of freelyavailable knowledge, with no costs. In recent work the effectiveness of these data points incontent-based and graph-based recommender systems was evaluated, and results showed thatLOD-based features can improve the accuracy of such systems. Finally, a very recent researchline investigated the adoption of techniques based on Deep Learning and Word Embeddings forContent-based Recommender Systems.

Some recent side projects focused on the development of framework for intelligent ContentAnalytics. Specifically, a pipeline relying on Entity Linking, Sentiment Analysis and MachineLearning techniques was developed. Such pipeline can extract real-time data streams fromsocial network and can process content in order to get some insights from the data, by exploitingwidespread techniques for data visualization. This platform has already been employed for theItalian Hate Map project.In this scenario, we built real-time maps where intolerant Tweetswere geolocalized. To this aim we exploited content-based semantic processing with sentimentanalysis to identify the most at-risk areas of the Italian territory.

4.2 Research Projects

• National Projects

1. CHAT - “Cultural Heritage fruition & e-learning applications of newAdvanced (multimodal) Technologies” (2006-2008).

The project aimed to implement multimodal information systems, able to provi-de users with rich user interfaces and to adapt their own behavior according touser interests and preferences. Cataldo Musto gave his contribution to the projectby implementing a content-based recommender system able to personalize museumtours.

2. MAIVISTO - Massive Adaptive Internet VIdeo STreaming Using theCloud (2014-2015).

The project, funded by the Italian Ministry of University and Research (MIUR),aimed to develop a platform for the fruition of multimedia content combining state-of-the-art techniques for adaptive video streaming with semantic methodologies forInformation Retrieval and Recommendation. Cataldo Musto gave his contributionby defining a content-based recommendation model to build a personalized videoplaylist.



3. VINCENTE - A “Virtual collective INtelligence Environment to de-velop sustainable Technology Entrepreneurship ecosystems” (2013-2014).

The project, funded by the Italian Ministry for the University and Research (MIUR),aimed to design, implement and test a methodological platform for the creation of asustainable entrepeneurship ecosystem which optimize the use of resources, enhancethe knowledge, respect the environment and ethical values and ensure social inclu-sion. Cataldo Musto gave his contribution by developing techniques for informationfiltering and recommendation which have been integrated in the platform.

• Regional Projects

1. PugliaDigitale 2.0 (2013-2015).

The goal of the project was to implement a cloud platform for the fruition of digitalservices made available by the companies belonging to the consortium. Specifically,the project made available a large catalogue of services and defined methodologies toautomatically combine atomic services in more intelligent and complex applications.Within the project, the candidate was responsible for the analysis of formalismsemantic annotation and discovery of services, and has defined a proposed model forthe suggestion of services.

2. Objectway Finance-as-a-Service (OFS)- ”‘Smart Application Softwareand Services for Financial Service Operatores”’ (2013-2014).

The project, jointly carried out with Objectway Financial Software, aimed at thedevelopment of a recommender systems for financial produces. The proposed ap-proach relies on the merge of case-based reasoning with diversification techniques.Cataldo Musto gave his contributions by defining the recommendation model as wellas implementing and evaluating the proposed approach.

3. ITSM-aoNET - ”‘IT Service Management Always on Net”’ (2012-2014).

The project, jointly carried out with IeT sistemi, Thesis, Infocom e Systech aimedat the development of an innovative platform for IT Service Management. CataldoMusto gave his contributions by developing some portlet for Social CRM based onTwitter, which facilitate the communication as well as the connction between ITSMusers and operators.

4. SISCApp - “Sistema Integrato a Supporto delle Comunita d’Apprendimento”(2009-2011).

The project aimed to design and implement the SISCAPP platform, a complexframework whose goal was to provide users with a personalized learning path, ac-cording to their knowledge and their interests. Cataldo Musto gave his contributionby implementing a content-based recommender systems able to recommend learningobjects.

5. SWOP - “Semantic Web Service Oriented Platform” (2009-2011).

The project aimed to implement a framework for the semantic and dynamic selec-tion,discovery, composition and invocation of web services. Cataldo Musto gave his



contribution by designing a recommendation module based on textual description toprovide users suggestions about web services they could be interested in.

6. HEALTHNET - The Health Network - Apulian ICT Living Lab (2014-2015).

The project aimed to develop a platform that allows the user to enter detailedinformation about her medical history in order to identify other patients who havepreviously suffered from illnesses or symptoms similar to her. Through this portal,the user can check the medical history of the other patients, the doctors consulted,the therapies they adopted, their success, and the evolution of the clinical historyof similar subjects over time. Within the project, Cataldo Musto focused on thedefinition of a model of recommendation can suggest other patients, medical facilitiesby taking into account clinical history, diseases and treatments of the users of theplatform.

7. PARS ECO - Apulian ICT Living Lab (2014-2015).

The project has the goal of preparing and testing a platform to support reuse anddisposal of rubbish materials. Within the project Cataldo Musto’s activies focusedon the definition of a suggestion algorithms which suggest the more appropriate wayto confer rubbish. Moreover, Linked Open Data methodologies was exploited topublish the content made available through the platform.

• Research Projects Funded by University of Bari

1. Member of the research unit for the national project Exploiting Open Know-ledge Sources in Content-based Recommender Systems (2009).

4.3 Partnership

Cataldo Musto had a primary role in the following parternships:

1. The Italian Hate Map - La Mappa dell’Intolleranza (2014-2015). The ItalianHate Map is a project jointly carried out with Vox - Italian Observatory on Human Rights,University of Rome La Sapienza and University of Milan. The goal of the project was tobuild a ”Italian Hate Map”, a map which discovers the most at-risk areas of the italiancountry, defined according to the content posted on social network. To this end, mapswere built by combining intelligent Content Analytics techniques to better understandthe semantics of the content and to understand the sentiment conveyed by discriminatorycontent.

5 Activities for the Scientific Community

5.1 Participation to Conferences and Workshops

Cataldo Musto gave some talks about the results of his research activities within the followingConferences and Workshops:



• Invited Talks and Tutorials:

1. Tutorial UMAP 2016 - 24th Conference on User Modeling, Adaptation and Perso-nalization, Halifax, Canada, July 16th 2016.

Tutorial: ”Pasquale Lops, Cataldo Musto. Semantics-Aware Techniques for SocialMedia Analysis, User Modeling, and Recommender Systems”

2. FinRec 2015 - 1st International Workshop on Personalization and RecommenderSystems in Financial Services, Graz, Austria, April 16th 2015.

Title of the talk: ”Cataldo Musto. Il progetto The Italian Hate Map contro leviolenze in Rete;

3. Event Smart Companies and Artificial Intelligence, Tecnologie Intelligential Servizio delle Aziende. Firenze, Italy, May 14th 2013.

Title of the talk: ”Giovanni Semeraro, Cataldo Musto. Recommender Systems diProdotti bancari/finanziari”;

4. SASWeb 2012 - 3rd International Workshop on Semantic, Adaptive and SocialWeb, Montreal, Canada, July 16-20th 2012.

Title of the talk: ”Cataldo Musto. Meaning is its use: towards the use of distribu-tional semantics for content-based recommender systems”.

• Presentation of Research Papers:

1. 24th International Conference on User Modeling, Adaptation and Personalization(UMAP 2016) - Title of the Talk: ”Semantics-aware Graph-based RecommenderSystems exploiting Linked Open Data”;

2. 1st CINI Annual Workshop on Smart Cities and Communities (i-Cities 2015) - Titleof the Talk: ”The Italian Hate Map: semantic content analytics for social good”;

3. 1st CINI Annual Workshop on Smart Cities and Communities (i-Cities 2015) - Titleof the Talk: ”Combining Social Data and Semantic Content Analysis for L’AquilaSocial Urban Network”;

4. 9th ACM Conference on Recommender Systems (RecSys 2015) - Title of the talk:”Automatic Selection of Linked Open Data features in Graph-based RecommenderSystems”’ during CBRECSYS 2015 workshop (2nd Workshop on New Trends inContent-based Recommender Systems);

5. 24th International World Wide Web Conference (WWW 2015) - Title of the Talk:”Developing Smart Cities services through Semantic Analysis of Social Streams”’during WDS4SC 2015 workshop (WWW2015 Workshop on Web Data Science andSmart Cities);

6. 14th Symposium of the Italian Association for Artificial Intelligence (AI*IA 2014) -Title of the talk: ”A comparison of Lexicon-based approaches for Sentiment Analysisof microblog posts” during DART 2014 workshop (8th International Workshop onInformation Filtering and Retrieval);



7. 8th ACM Conference on Recommender Systems (RecSys 2014) - Title of the talk:”Linked Open Data-enabled Strategies for Top-N Recommendations”’ during CBREC-SYS 2014 workshop (1st Workshop on New Trends in Content-based RecommenderSystems);

8. 22th International Conference on User Modeling, Adaptation and Personalization(UMAP 2014) - Title of the talk: ”Combining Distributional Semantics and EntityLinking for Context-Aware Content-Based Recommendation”;

9. 5th Italian Information Retrieval Workshop (IIR 2014) - Title of the talk: ”Develo-ping a Semantic Content Analyzer for L’Aquila Social Urban Network”;

10. 14th International Conference on Electronic Commerce and Web Technologies (EC-WEB 2013) - Title of the talk: ”Contextual eVSM: A Content-Based Context-AwareRecommendation Framework Based on Distributional Semantics”;

11. 4th Italian Information Retrieval Workshop (IIR 2013) - Title of the talk: ”Distri-butional Models vs. Linked Data: Exploiting Crowdsourcing to Personalize MusicPlaylists”;

12. 13th International Conference on Electronic Commerce and Web Technologies (EC-WEB 2012) - Title of the talk: ”Leveraging Social Media Sources to GeneratePersonalized Music Playlists”;

13. 20th International Conference on User Modeling, Adaptation and Personalization(UMAP 2012) - Title of the talk: ”Enhanced Semantic TV-Show Representation forPersonalized Electronic Program Guides”;

14. 3rd Italian Information Retrieval Workshop (IIR 2012) - Title of the talk: ”Com-paring Word Sense Disambiguation and Distributional Models for Cross-LanguageInformation Filtering”;

15. 1st Workshop on Personalized Multimedia Hypertext Retrieval (PMHR 2011) -Title of the talk: ”Learning Semantic Content-based Profiles for Cross-languagePersonalization”;

16. 11th Symposium of the Italian Association for Artificial Intelligence (AI*IA 2011) -Title of the talk: ”Cross-Language Information Filtering: Word Sense Disambigua-tion vs. Distributional Models”;

17. 12th International Conference on Electronic Commerce and Web Technologies (EC-WEB 2011) - Title of the talk: ”Random Indexing and Negative User Preferencesfor Enhancing Content-Based Recommender Systems”;

item 2nd Italian Information Retrieval Workshop (IIR 2011) - Title of the talk:”Random Indexing for Content-Based Recommender Systems”;

18. 4th ACM Conference on Recommender Systems (RecSys 2010) - Title of the talk:”Enhanced vector space models for content-based recommender systems”;

item 1st Italian Information Retrieval Workshop (IIR 2010) - Title of the talk: ”AnIR-Based Approach for Tag Recommendation”;



19. 11th International Conference on Electronic Commerce and Web Technologies (EC-WEB 2010) - Title of the talk: ”Combining Collaborative and Content-Based Tech-niques for Tag Recommendation”;

20. 1st AI*IA 2009 Workshop on Intelligent Cultural Heritage - Title of the talk: ”Content-based Recommendation Services for Personalized Fruition of Cultural Heritage”;

21. 4th International Conference on Automated Solutions for Cross Media Content andMulti-channel Distribution (AXMEDIS 2008) - Title of the talk: ”FIRSt: a Content-based Recommender System Integrating Tags for Cultural Heritage Personalization”;

• Public Events:

1. Event FuoriCorso” - Melkweg. Terlizzi (BA), Italy, March 9th 2016.

Title of the talk: ”Cataldo Musto. L’irragionevole efficacia dei dati”;

2. Event Presentazione eBook ”La Rete e il Fattore C: competenze, consa-pevolezza, conoscenze. Matera, Italy, November 25th 2014.

Title of the talk: ”Cataldo Musto. Il progetto della Mappa dell’Intolleranza controle violenze in Rete”;

5.2 Conference Organization

• Workshop Chair for:

1. CBRecSys 2016 - 3rd Workshop on New Trends in Content-based Recommender Sy-stems, co-located with the 9th ACM Conference on Recommender Systems (RECSYS2016);

2. DeCAT 2015 - 1st Workshop on Deep Content Analytics techniques for Personalizedand Intelligent Services, co-located with the 23th International Conference on UserModeling, Adaptation and Personalization (UMAP 2015);

• Publicity Chair for:

1. ACM UMAP 2017 - 25th International Conference on User Modeling, Adaptationand Personalization;

2. EC-WEB 2013 - 14th International Conference on Electronic Commerce and WebTechnologies;

• Local Organization for:

1. FINREC 2016 - 2nd International Workshop on Personalization and RecommenderSystems in Financial Services;

2. IIR 2012 - 3rd Italian Information Retrieval Workshop;

3. SWAP 2007 - 4th Italian Workshop on “Semantic Web Applications and Perspecti-ves”;



5.3 Editorial Activities

• Giovanni Semeraro, Cataldo Musto, Mathias Bauer: Proceedings of the 2nd Internatio-nal Workshop on Personalization & Recommender Systems in Financial Services, Bari,Italy, June 16, 2016. CEUR Workshop Proceedings 1606

• Toine Bogers, Marijn Koolen, Cataldo Musto, Pasquale Lops, Giovanni Semeraro: Pro-ceedings of the 3rd Workshop on New Trends in Content-Based Recommender Systemsco-located with ACM Conference on Recommender Systems (RecSys 2016), Boston, MA,USA, September 16, 2016. CEUR Workshop Proceedings 1673, CEUR-WS.org 2016

• Member of the Editorial Board of RecSys Review (http://www.recommenders.net/review),official newsletter of Recommender Systems community, since 2014.

5.4 Program Commitees

• Member of the Program Committee for:

1. 10th ACM Conference on Recommender Systems (ACM RecSys 2016);

2. 10th ACM Conference on Recommender Systems - Demo and Poster Track (ACMRecSys 2016);



5. 24th ACM Conference on User Modeling, Adaptation and Personalization (ACMUMAP 2016);

6. 24th ACM Conference on User Modeling, Adaptation and Personalization - PosterTrack (ACM UMAP 2016);

7. 2a Conferenza Italiana di Linguistica Computazionale (Clic-it 2015)

8. 1a Conferenza Italiana di Linguistica Computazionale (Clic-it 2014)

9. 16th International Conference on Electronic Commerce and Web Technologies (EC-WEB 2015);



12. 22th International Conference on Intelligent User Interfaces (IUI 2017);

13. 3rd International Workshop on Educational Recommender Systems (EDRecSys 2016);

14. 2nd International Workshop on Knowledge Discovery on the Web (KDWeb 2016);

15. 1st International Workshop on Knowledge Discovery on the Web (KDWeb 2015);

16. 2nd International Workshop on Recommender Systems meet Big Data and SemanticTechnologies (SeRSy 2013);



17. 1st International Workshop on Recommender Systems meet Big Data and SemanticTechnologies (SeRSy 2012);

18. 7th Italian Information Retrieval Workshop (IIR 2016);


20. 10th Italian Workshop on Artificial Intelligence for Cultural Heritage (AI*CH 2016)

21. 38th Language Resources and Evaluation Conference (LREC 2016);


23. 32nd ACM Symposium on Applied Computing (ACM SAC 2017)

24. 2nd Workshop on New Trends in Content-based Recommender Systems (CBRECSYS2015)

25. 3st Workshop on Personalization in eGovernment Services and Applications (PEGOV2015);



28. 3rd Workshop on Recommender Systems meet Databases (RSmeetDB 2013).

5.5 Peer Reviewing

• External reviewer for the following conferences and workshops.

1. 20th ACM Conference on Hypertext and Hypermedia (Hypertext 2009);






7. 8th ACM Conference on Recommender Systems (RecSys 2014 - Demo and PosterTrack);

8. AI*IA Workshop on Artificial Intelligence and Human-Computer Interfaces (AI*HCI2013);

9. 13th Symposium of the Italian Association for Artificial Intelligence (AI*IA 2013);

10. 39th European Conference on Information Retrieval (ECIR 2017);







15. 18th European Conference on Machine Learning and Principles and Practice ofKnowledge Discovery in Databases (ECML PKDD 2016);




19. 11th European Semantic Web Conference (ESWC 2014);

20. 5th International Conference on Advanced Data Mining and Applications (ADMA2009);

21. 14th International Conference on Artificial Intelligence: methodology, systems, ap-plications (AIMSA 2010);

22. 4th International Conference on Cloud Computing and Services Science (CLOSER2014);




26. 11th International Conference on Intelligent System Design and Applications (ISDA2011);

27. 9th International Conference on Intelligent System Design and Applications (ISDA2009);

28. 19th International Conference on Intelligent User Interfaces (IUI 2014);

29. 7th International Conference on Knowledge Capture (K-CAP 2013);

30. 23th International Conference on User Modeling, Adaptation and Personalization(UMAP 2015);



33. 6th International Conference on Web Search and Data Mining (WSDM 2013);

34. International Workshop on Adaptation in Social and Semantic Web (SASWeb 2010);

35. 2nd International Workshop on Interfaces and Human Decision-Making for Recom-mender Systems (IntRS 2015);



36. 1st International Workshop on Recommender Systems meet Big Data and SemanticTechnologies (SeRSy 2012);

37. International Workshop on Semantic Adaptive Social Web (SASWeb 2011);

38. International Workshop on Web Personalization and Recommender Systems (Web-Pres 2010) - Joint workshop with the WI-IAT Conference;

39. 3rd International Workshop on Web Personalization, Recommender Systems andSocial Media (WPRSM 2012);


41. 1st Italian Information Retrieval Workshop (IIR 2010);



44. Learning from User-Generated Content Workshop at ICML 2012 (LUC 2012);

45. Mining Ubiquitous and Social Environments (MUSE 2011);

46. Mining Ubiquitous and Social Environments (MUSE 2010);

47. 16th Online Conference on Soft Computing in Industrial Applications (WSC16 2011);

48. 2nd Workshop on Semantic Personalized Information Management (SPIM 2011);

49. 6th Workshop on Semantic Web Applications and Perspectives (SWAP 2010);

• Reviewer for the following journals;

1. ACM TIST - ACM Transactions on Intelligent Systems and Technology (2011);

2. ACM TOIS - ACM Transactions on Intelligent Systems (2014);

3. ACM TOIT - ACM Transactions on Internet Technology (2016);

4. Artificial Intelligence Journal - Special Issue on ”Artificial Intelligence, Wikipediaand Semi-Structured Resources (2010);

5. ID&A - Interactive Design and Architectures (2016);

6. IEEE Communication Magazine (2014);

7. IEEE Internet Computing (2010);

8. IJHCS - International Journal of Human-Computer Studies (2014);

9. IJEPR - International Journal of E-Planning Research (2016);

10. IJSWIS - International Journal of Semantic Web Information Services (2015);

11. ECRA - Electronic Commerce Research and Applications (2012);

12. JMLC - International Journal of Machine Learning and Cybernetics (2015);

13. JZUSC - Frontiers of Information Technology & Electronic Engineering (2016);

14. PMC - Pervasive and Mobile Computing (2015);

15. SCICO - Science of Computer Programming (2013);

16. The Computer Journal (2016);



5.6 Associations

• Member of SWAP Research Group (Semantic Web Access & Personalization) of the De-partment of Computer Science of University of Bari, head by prof. Giovanni Semeraro,dal 2008. (website: http://www.di.uniba.it/ swap)

• Member of’AI*IA, Italian Association for Artificial Intelligence, since 2008.

6 Prizes

• October 2014: Winner of the research grant Future in Research, funded by RegionePuglia. The grant was won with a project proposal entitled: Semantic Holistic UserModeling for Personalized Access to Digital Services and Content.

• May 2014: Winner of the Linked Open Data-enabled Recommender SystemsChallenge, organized within the 11a European Semantic Web Conference (ESWC 2014),with the framework described in the paper Content-Based Recommender Systems +DBpedia Knowledge = Semantics-Aware Recommender Systems.

• June 2013: Most Inspiring Contribution Award, obtained during the conferenceUMAP 2013 (21st International Conference on User Modeling, Adaptation and Persona-lization) with a paper entitled: Leveraging Encyclopedic Knowledge for Transparent andSerendipitous User Profiles.

• August 2010: Best Project Work, obtained during the IEEE SSSC 2010 SummerSchool . The prize was given thanks to the development of a system for the suggestionof personalized music playlists based on Linked Data.



7 Publications

7.1 Summary

Cataldo Musto is author of 60 publications:

• n. 10 Journal Papers;

• n. 3 Chapters in International Books;

• n. 22 Articles in International Conferences;

• n. 1 Article in National Conferences;

• n. 12 Articles in International Workshops:

• n. 11 Articles in National Workshops:

47 out of 58 publications are indexed on DBLP1.

7.2 Scientific Impact

Metrics have been calculated on November 3, 2016, by using the tool Harzing, A.W. Publishor Perish, available for download www.harzing.com/pop.htm.

• h-index: 11

• g-index: 16

• Citations: 382

• Years: 8

• Cites/year: 47.75

• Cites/paper: 6.26

7.3 List of Publications

Journal Articles

[JA.1] F. Narducci, P. Basile, C. Musto, P. Lops, A. Caputo, M. de Gemmis,L. Iaquinta, and G. Semeraro. Concept-based item representations for a cross-lingualcontent-based recommendation process. Information Sciences , 374:1339–1351, 2016.

[JA.2] M. de Gemmis, P. Lops, G. Semeraro, and C. Musto. An investigation on theserendipity problem in recommender systems. Information Processing and Management ,51 (5):695–717, 2015.

1http://www.informatik.uni-trier.de/ ley/db/indices/a-tree/m/Musto:Cataldo.html



[JA.3] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops. Crowdpulse: a frameworkfor real-time semantic analysis of social streams. Information Systems , 54:127–146, 2015.

[JA.4] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops. A framework for Perso-nalized Wealth Management exploiting Case-based Recommender Systems. IntelligenzaArtificiale - Special Issue on Artificial Intelligence for Society and Economy , 9 (1):89–103,2015. ISSN 1724-8035.

[JA.5] C. Musto, G. Semeraro, P. Lops, M. de Gemmis, and G. Lekkas. Personalizedfinance advisory through case-based recommender systems and diversification strategies.Decision Support Systems , 77:100–111, 2015.

[JA.6] L. Bordoni, L. Ardissono, J. A. Barcelo, A. Chella, M. de Gemmis, C. Ge-na, L. Iaquinta, P. Lops, F. Mele, C. Musto, F. Narducci, G. Semeraro, andA. Sorgente. The contribution of ai to enhance understanding of cultural heritage.Intelligenza Artificiale, 7(2):101–112, 2013. ISSN 1724-8035.

[JA.7] P. Lops, M. de Gemmis, G. Semeraro, C. Musto, and F. Narducci. Content-based and collaborative techniques for tag recommendation: an empirical evaluation.Journal Of Intelligent Information Systems , 40(1):41–61, 2013. ISSN 0925-9902.

[JA.8] G. Semeraro, P. Basile, R. Basili, M. de Gemmis, C. Ghidini, M. Lenzerini,P. Lops, A. Moschitti, C. Musto, F. Narducci, A. Pipitone, R. Pirrone,P. Poccianti, and L. Serafini. Semantic technologies for industry: From knowledgemodeling and integration to intelligent applications. Intelligenza Artificiale, 7(2):125–137,2013. ISSN 1724-8035.

[JA.9] G. Semeraro, P. Lops, M. de Gemmis, C. Musto, and F. Narducci. Afolksonomy-based recommender system for personalized access to digital artworks. ACMJournal of Computing and Cultural Heritage (JOCCH), 5(3):11, 2012. ISSN 1556-4673.

Book Chapters

[BC.1] M. De Gemmis, P. Lops, C. Musto, F. Narducci, and G. Semeraro. Semantics-aware content-based recommender systems . 2015.

[BC.2] M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, and G. Seme-raro. Learning Preference Models in Recommender Systems. In E. Hullermeier andJ. Fuernkranz, editors, Preference Learning , pages 387–407. Springer, 2011. ISBN978-3-642-14125-6.

[BC.3] P. Lops, M. de Gemmis, G. Semeraro, C. Musto, F. Narducci, and M. Bux.A semantic content-based recommender system integrating folksonomies for personalizedaccess. In G. Castellano, L. C. Jain, and A. M. Fanelli, editors, Web Per-sonalization in Intelligent Environment , pages 27–47. Springer (Berlin), 2009. ISBN978-3-642-02793-2.



International Conferences

[IC.1] C. Musto, P. Lops, P. Basile, M. de Gemmis, and G. Semeraro. Semantics-aware graph-based recommender systems exploiting linked open data. In J. Vassile-va, J. Blustein, L. Aroyo, and S. K. D’Mello, editors, Proceedings of the 2016Conference on User Modeling Adaptation and Personalization, UMAP 2016, Halifax, NS,Canada, July 13 - 17, 2016 , pages 229–237. ACM, 2016. ISBN 978-1-4503-4368-8.

[IC.2] C. Musto, F. Narducci, P. Lops, M. de Gemmis, and G. Semeraro. Explod:A framework for explaining recommendations based on the linked open data cloud. InS. Sen, W. Geyer, J. Freyne, and P. Castells, editors, Proceedings of the 10thACM Conference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 ,pages 151–154. ACM, 2016. ISBN 978-1-4503-4035-9.

[IC.3] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops. Learning word embeddingsfrom wikipedia for content-based recommender systems. In N. Ferro, F. Crestani,M. Moens, J. Mothe, F. Silvestri, G. M. D. Nunzio, C. Hauff, and G. Silvel-lo, editors, Advances in Information Retrieval - 38th European Conference on IR Research,ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings , volume 9626 of Lecture Notesin Computer Science, pages 729–734. Springer, 2016. ISBN 978-3-319-30670-4.

[IC.4] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops. Modeling communitybehavior through semantic analysis of social data: The italian hate map experience. InJ. Vassileva, J. Blustein, L. Aroyo, and S. K. D’Mello, editors, Proceedings of the2016 Conference on User Modeling Adaptation and Personalization, UMAP 2016, Halifax,NS, Canada, July 13 - 17, 2016 , pages 307–308. ACM, 2016. ISBN 978-1-4503-4368-8.

[IC.5] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops. Developing smart citiesservices through semantic analysis of social streams. In A. Gangemi, S. Leonardi, andA. Panconesi, editors, Proceedings of the 24th International Conference on World WideWeb Companion, WWW 2015, Florence, Italy, May 18-22, 2015 - Companion Volume,pages 1401–1406. ACM, 2015. ISBN 978-1-4503-3473-0.

[IC.6] C. Musto, G. Semeraro, M. de Gemmis, and P. Lops. Word embedding techni-ques for content-based recommender systems: An empirical evaluation. In P. Castells,editor, Poster Proceedings of the 9th ACM Conference on Recommender Systems, RecSys2015, Vienna, Austria, September 16, 2015., volume 1441 of CEUR Workshop Proceedings .CEUR-WS.org, 2015.

[IC.7] F. Narducci, C. Musto, M. Polignano, M. de Gemmis, P. Lops, and G. Se-meraro. A recommender system for connecting patients to the right doctors in thehealthnet social network. In A. Gangemi, S. Leonardi, and A. Panconesi, editors,Proceedings of the 24th International Conference on World Wide Web Companion, WWW2015, Florence, Italy, May 18-22, 2015 - Companion Volume, pages 81–82. ACM, 2015.ISBN 978-1-4503-3473-0.



[IC.8] C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Combining distribu-tional semantics and entity linking for context-aware content-based recommendation. InV. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog, and G. Houben, editors,User Modeling, Adaptation, and Personalization - 22nd International Conference, UMAP2014, Aalborg, Denmark, July 7-11, 2014. Proceedings , volume 8538 of Lecture Notes inComputer Science, pages 381–392. Springer, 2014. ISBN 978-3-319-08785-6.

[IC.9] C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Contextual eVSM: Acontent-based context-aware recommendation framework based on distributional seman-tics. In C. Huemer and P. Lops, editors, EC-Web, volume 152 of Lecture Notes inBusiness Information Processing , pages 125–136. Springer, 2013. ISBN 978-3-642-39877-3.

[IC.10] F. Narducci, C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Ex-ploiting big data for enhanced representations in content-based recommender systems. InC. Huemer and P. Lops, editors, EC-Web, volume 152 of Lecture Notes in BusinessInformation Processing , pages 182–193. Springer, 2013. ISBN 978-3-642-39877-3.

[IC.11] F. Narducci, C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Levera-ging encyclopedic knowledge for transparent and serendipitous user profiles. In S. Car-berry, S. Weibelzahl, A. Micarelli, and G. Semeraro, editors, UMAP , volu-me 7899 of Lecture Notes in Computer Science, pages 350–352. Springer, 2013. ISBN978-3-642-38843-9.

[IC.12] C. Musto, F. Narducci, P. Lops, G. Semeraro, M. de Gemmis, M. Barbieri,J. H. M. Korst, V. Pronk, and R. Clout. Enhanced semantic tv-show represen-tation for personalized electronic program guides. In J. Masthoff, B. Mobasher,M. C. Desmarais, and R. Nkambou, editors, UMAP , volume 7379 of Lecture Notesin Computer Science, pages 188–199. Springer, 2012. ISBN 978-3-642-31453-7.

[IC.13] C. Musto, G. Semeraro, P. Lops, M. de Gemmis, and F. Narducci. Le-veraging social media sources to generate personalized music playlists. In C. Huemerand P. Lops, editors, EC-Web, volume 123 of Lecture Notes in Business InformationProcessing , pages 112–123. Springer, 2012. ISBN 978-3-642-32272-3.

[IC.14] P. Lops, M. de Gemmis, G. Semeraro, F. Narducci, and C. Musto. Le-veraging the linkedin social network data for extracting content-based user profiles. InB. Mobasher, R. D. Burke, D. Jannach, and G. Adomavicius, editors, RecSys ,pages 293–296. ACM, 2011. ISBN 978-1-4503-0683-6.

[IC.15] C. Musto, F. Narducci, P. Basile, P. Lops, M. de Gemmis, and G. Semera-ro. Cross-language information filtering: Word sense disambiguation vs. distributionalmodels. In R. Pirrone and F. Sorbello, editors, AI*IA, volume 6934 of Lecture Notesin Computer Science, pages 250–261. Springer, 2011. ISBN 978-3-642-23953-3.

[IC.16] C. Musto, G. Semeraro, P. Lops, and M. de Gemmis. Random indexingand negative user preferences for enhancing content-based recommender systems. InC. Huemer and T. Setzer, editors, EC-Web, volume 85 of Lecture Notes in BusinessInformation Processing , pages 270–281. Springer, 2011. ISBN 978-3-642-23013-4.



[IC.17] P. Lops, C. Musto, F. Narducci, M. de Gemmis, P. Basile, and G. Seme-raro. Cross-language personalization through a semantic content-based recommendersystem. In D. Dicheva and D. Dochev, editors, AIMSA, volume 6304 of LectureNotes in Computer Science, pages 52–60. Springer, 2010. ISBN 978-3-642-15430-0.

[IC.18] C. Musto. Enhanced vector space models for content-based recommender systems.In Proceedings of the fourth ACM conference on Recommender systems , RecSys ’10, pages361–364. ACM, New York, NY, USA, 2010. ISBN 978-1-60558-906-0.

[IC.19] C. Musto, F. Narducci, P. Lops, and M. de Gemmis. Combining collabo-rative and content-based techniques for tag recommendation. In F. Buccafurri andG. Semeraro, editors, EC-Web, volume 61 of Lecture Notes in Business InformationProcessing , pages 13–23. Springer, 2010. ISBN 978-3-642-15207-8.

[IC.20] P. Basile, M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci,and G. Semeraro. Spiter: A module for recommending dynamic personalized museumtours. In 2009 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2009,Milan, Italy, 15-18 September 2009, Main Conference Proceedings , pages 584–587. IEEEComputer Society, 2009. ISBN 978-0-7695-3801-3.

[IC.21] C. Musto, F. Narducci, P. Lops, M. de Gemmis, and G. Semeraro. Content-Based Personalization Services Integrating Folksonomies. In E-Commerce and Web Tech-nologies, 10th International Conference, EC-Web 2009, Linz, Austria, September 1-4,2009. Proceedings , volume 5692 of Lecture Notes in Computer Science, pages 217–228.Springer, 2009. ISBN 978-3-642-03963-8.

[IC.22] P. Lops and M. de Gemmis and G. Semeraro and P. Gissi and C. Mustoand F. Narducci. Content-based Filtering with Tags: the FIRSt System. In NinthInternational Conference on Intelligent Systems Design and Applications, ISDA 2009, Pi-sa, Italy, November 30-December 2, 2009 , pages 255–260. IEEE Computer Society, LOSALAMITOS, CA – USA, 2009. ISBN 978-0-7695-3872-3.

National Conferences

[AN.1] C. Musto, F. Narducci, P. Lops, M. de Gemmis, and G. Semeraro. Inte-grating a content-based recommender system into digital libraries for cultural heritage.In M. Agosti, F. Esposito, and C. Thanos, editors, IRCDL, volume 91 of Com-munications in Computer and Information Science, pages 27–38. Springer, 2010. ISBN978-3-642-15849-0.



International Workshops

[IW.1] C. Musto, P. Basile, M. de Gemmis, P. Lops, G. Semeraro, and S. Ruti-gliano. Automatic selection of linked open data features in graph-based recommendersystems. In T. Bogers and M. Koolen, editors, Proceedings of the 2nd Workshopon New Trends on Content-Based Recommender Systems co-located with 9th ACM Confe-rence on Recommender Systems (RecSys 2015), Vienna, Austria, September 16-20, 2015.,volume 1448 of CEUR Workshop Proceedings , pages 10–13. CEUR-WS.org, 2015.

[IW.2] C. Musto and G. Semeraro. Case-based recommender systems for personalizedfinance advisory. In A. Felfernig, J. Tiihonen, and P. Blazek, editors, Proceedingsof the 1st International Workshop on Personalization & Recommender Systems in Finan-cial Services, Graz, Austria, April 16, 2015., volume 1349 of CEUR Workshop Proceedings ,pages 35–36. CEUR-WS.org, 2015.

[IW.3] P. Basile, C. Musto, M. de Gemmis, P. Lops, F. Narducci, and G. Semera-ro. Content-based recommender systems + dbpedia knowledge = semantics-aware recom-mender systems. In V. Presutti, M. Stankovic, E. Cambria, I. Cantador, A. D.Iorio, T. D. Noia, C. Lange, D. R. Recupero, and A. Tordai, editors, SemanticWeb Evaluation Challenge - SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece,May 25-29, 2014, Revised Selected Papers , volume 475 of Communications in Computerand Information Science, pages 163–169. Springer, 2014. ISBN 978-3-319-12023-2.

[IW.4] C. Musto, P. Basile, P. Lops, M. de Gemmis, and G. Semeraro. Linked opendata-enabled strategies for top-n recommendations. In T. Bogers, M. Koolen, andI. Cantador, editors, Proceedings of the 1st Workshop on New Trends in Content-basedRecommender Systems co-located with the 8th ACM Conference on Recommender Systems,CBRecSys@RecSys 2014, Foster City, Silicon Valley, California, USA, October 6, 2014.,volume 1245 of CEUR Workshop Proceedings , pages 49–56. CEUR-WS.org, 2014.

[IW.5] C. Musto, G. Semeraro, and M. Polignano. A comparison of lexicon-basedapproaches for sentiment analysis of microblog posts. Proceedings of the 8th InternationalWorkshop on Information Filtering and Retrieval co-located with XIII AI*IA Symposiumon Artificial Intelligence (AI*IA 2014), pages 59–68, 2014.

[IW.6] P. Lops, C. Musto, F. Narducci, M. de Gemmis, P. Basile, and G. Seme-raro. Learning semantic content-based profiles for cross-language personalization. InProceedings of the Workshop on Personalized Multimedia Hypertext Retrieval (PMHR11),pages 26–33. June 6 2011.

[IW.7] P. Lops, C. Musto, F. Narducci, M. de Gemmis, P. Basile, and G. Semera-ro. MARS:a multilanguage recommender system. In Proceedings of the 1st InternationalWorkshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec2010), pages 24–31. September 26 2010.

[IW.8] M. de Gemmis, L. Iaquinta, P. Lops, C. Musto, F. Narducci, and G. Seme-raro. Preference Learning in Recommender Systems. In Proceedings of the ECML/PKDD2009 Workshop on Preference Learning , pages 41–55. September 11 2009.



[IW.9] C. Musto, F. Narducci, M. de Gemmis, P. Lops, and G. Semeraro. ATag Recommender System Exploiting User and Community Behavior. In D. Jannach,W. Geyer, J. Freyne, S. S. Anand, C. Dugan, B. Mobasher, and A. Kobsa,editors, Proceedings of the ACM RecSys 2009 Workshop on Recommender Systems & TheSocial Web, volume 532 of CEUR Workshop Proceedings , pages 25–33. NEW YORK,October 25 2009. ISSN 1613-0073.

[IW.10] C. Musto, F. Narducci, M. de Gemmis, P. Lops, and G. Semeraro. STaR: aSocial Tag Recommender System. In F. Eisterlehner, A. Hotho, and R. Jaschke,editors, ECML PKDD Discovery Challenge 2009 (DC09), volume 497 of CEUR WorkshopProceedings , pages 215–227. September 7 2009. ISSN 1613-0073.

[IW.11] P. Basile, F. Calefato, M. de Gemmis, P. Lops, G. Semeraro, M. Bux,C. Musto, and F. Narducci. Augmenting a Content-based Recommender System withTags for Cultural Heritage Personalization. In L. Aroyo, T. Kuflik, O. Stock, andM. Zancanaro, editors, Proceedings of the Workshop on Personalized Access to CulturalHeritage (PATCH 2008) at the 5th International Conference on Adaptive Hypermedia andAdaptive Web-Based Systems (AH 2008), Hannover, Germany , pages 25–34. July 29,2008.

[IW.12] P. Basile, M. de Gemmis, P. Lops, G. Semeraro, M. Bux, C. Musto, andF. Narducci. First: a content-based recommender system integrating tags for culturalheritage personalization. In Proceedings of the 4th International Conference on AutomatedSolutions for Cross Media Content and Multi-channel Distribution (AXMEDIS 2008) -Workshop on Cultural heritage and Artificial Intelligence, Florence, Italy, 17-19 November,2008 . 2008.

National Workshops

[NW.1] C. Musto, C. Greco, A. Suglia, and G. Semeraro. Ask me any rating: Acontent-based recommender system based on recurrent neural networks. In G. M. D.Nunzio, F. M. Nardini, and S. Orlando, editors, Proceedings of the 7th ItalianInformation Retrieval Workshop, Venezia, Italy, May 30-31, 2016., volume 1653 of CEURWorkshop Proceedings . CEUR-WS.org, 2016.

[NW.2] C. Musto, G. Semeraro, P. Lops, M. de Gemmis, F. Narducci, L. Bordoni,M. Annunziato, C. Meloni, F. F. Orsucci, and G. Paoloni. Developing a seman-tic content analyzer for l’aquila social urban network. In R. Basili, F. Crestani, andM. Pennacchiotti, editors, Proceedings of the 5th Italian Information Retrieval Work-shop, Roma, Italy, January 20-21, 2014., volume 1127 of CEUR Workshop Proceedings ,pages 34–38. CEUR-WS.org, 2014.

[NW.3] C. Musto, F. Narducci, G. Semeraro, P. Lops, and M. de Gemmis. Distri-butional models vs. linked data: Exploiting crowdsourcing to personalize music playlists.In R. Basili, F. Sebastiani, and G. Semeraro, editors, IIR, volume 964 of CEURWorkshop Proceedings , pages 84–87. CEUR-WS.org, 2013.



[NW.4] C. Musto, F. Narducci, G. Semeraro, P. Lops, and M. de Gemmis. Myusic:a content-based music recommender system based on eVSM and social media. In R. Ba-sili, F. Sebastiani, and G. Semeraro, editors, IIR, volume 964 of CEUR WorkshopProceedings , pages 65–72. CEUR-WS.org, 2013.

[NW.5] C. Musto, F. Narducci, P. Basile, P. Lops, M. de Gemmis, and G. Semera-ro. Comparing word sense disambiguation and distributional models for cross-languageinformation filtering. In G. Amati, C. Carpineto, and G. Semeraro, editors, IIR,volume 835 of CEUR Workshop Proceedings , pages 117–120. CEUR-WS.org, 2012.

[NW.6] C. Musto, F. Narducci, P. Lops, G. Semeraro, M. de Gemmis, M. Barbieri,J. H. M. Korst, V. Pronk, and R. Clout. Tv-show retrieval and classification. InG. Amati, C. Carpineto, and G. Semeraro, editors, IIR, volume 835 of CEURWorkshop Proceedings , pages 179–182. CEUR-WS.org, 2012.

[NW.7] C. Musto, P. Lops, M. de Gemmis, and G. Semeraro. Random indexing forcontent-based recommender systems. In M. Melucci, S. Mizzaro, and G. Pasi,editors, IIR, volume 704 of CEUR Workshop Proceedings . CEUR-WS.org, 2011.

[NW.8] C. Musto. Boosting Content-based Recommender Systems through Advanced VectorSpace Models. In Abstract Booklet of the 1st AI*IA Doctoral Consortium, pages 87–91.December 1-3 2010.

[NW.9] C. Musto, F. Narducci, M. de Gemmis, P. Lops, and G. Semeraro. An IR-based approach for tag recommendation. In M. Melucci, S. Mizzaro, and G. Pasi,editors, IIR, volume 560 of CEUR Workshop Proceedings , pages 65–69. CEUR-WS.org,2010.

[NW.10] C. Musto, F. Narducci, M. de Gemmis, P. Lops, and G. Semeraro. Content-based recommendation services for personalized fruition of cultural heritage. In Procee-dings of the AI*IA 2009 Workshop on Intelligent Cultural Heritage. 2009.

[NW.11] P. Basile, F. Calefato, M. de Gemmis, P. Lops, G. Semeraro, M. Bux,C. Musto, and F. Narducci. Cultural Heritage Personalization using a Content-basedRecommender System and Folksonomies. In G. Armano, M. Schaerf, and G. Seme-raro, editors, Atti del Convegno dell’Associazione Italiana per l’Intelligenza Artificiale,Workshop Artificial Intelligence and Cultural Heritage, pages 46–54. September 11, 2008.

Miscellaneous

[1] T. Bogers, M. Koolen, C. Musto, P. Lops, and G. Semeraro. Third work-shop on new trends in content-based recommender systems (cbrecsys 2016). In S. Sen,W. Geyer, J. Freyne, and P. Castells, editors, Proceedings of the 10th ACMConference on Recommender Systems, Boston, MA, USA, September 15-19, 2016 , pages419–420. ACM, 2016.

[2] C. Musto, G. Semeraro, P. Lops, M. de Gemmis, and G. Lekkas. Financialproduct recommendation through case-based reasoning and diversification techniques. In



L. Chen and J. Mahmud, editors, Poster Proceedings of the 8th ACM Conference onRecommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, October 6-10,2014 , volume 1247 of CEUR Workshop Proceedings . CEUR-WS.org, 2014.

Bari, 10 gennaio 2017Cataldo Musto, Ph.D.


Documents

Curriculum Vitae Cataldo Musto, Ph.D.swap/musto/musto_cv.pdf · data sylos, it is possible to feed content-based adaptive platform with a huge amount of freely available knowledge,