Invited talk

Professor Giovanni Semeraro
Content-based Recommender Systems: problems, challenges and current research directions

Content-based recommender systems (CBRS) analyze a set of objects, usually textual descriptions of items previously rated by a user, and build a model of user interests, called user profile, based on the features of the objects rated by that user. The user profile is then exploited to recommend new potentially relevant items.

In spite of the growing importance of collaborative filtering algorithms over the last years, Web 2.0 and the huge amount of user generated content, such as tags, annotations, folksonomies, etc., are providing new opportunities and challenges for CBRS.

The talk discusses the main problems which cause some limitations of CBRS, such as overspecialization and limited availability of content, and describes current research directions for overcoming them, including:

• defeating homophily in recommender systems: introducing serendipity for recommendation diversification
• knowledge infusion into CBRS: exploiting open knowledge sources (Wikipedia, folksonomies) for improving recommendation algorithms
• cross-language recommender systems: algorithms for learning multilingual content-based profiles

Short Bio

Giovanni Semeraro is Associate Professor at the University of Bari “Aldo Moro” since November 1, 1998, where he leads the research group SWAP (Semantic Web Access & Personalization, http://www.di.uniba.it/~swap/) at the Department of Computer Science.
His main research interests fall into the following areas:

• Intelligent Information Access
• Recommender Systems
• Information Mining
• Machine Learning
• Personalization and User Modelling
• Natural Language Processing

He served as scientific responsible for 10 international and national projects and 16 research contracts.
He is editor of 8 international books and author of more than 300 scientific papers published in international journals, books, conference and workshop proceedings.