Timeslot Title Type of activity
09:00-09:05 Opening
09:05-10:00 Keynote: Semantics-aware Content-based Recommender Systems (Pasquale Lops) Keynote
10:00-10:30 Linked Open Data-enabled Strategies for Top-N Recommendations (Cataldo Musto, Pierpaolo Basile, Pasquale Lops, Marco De Gemmis and Giovanni Semeraro) Long paper
10:30-11:00 Coffee break
11:00-11:30 Exploiting FrameNet for Content-Based Book Recommendation (Orphée De Clercq, Michael Schuhmacher, Simone Paolo Ponzetto and Veronique Hoste, slides) Long paper
11:30-11:50 Preference Mapping for Automated Recommendation of Product Attributes for Designing Marketing Content (Moumita Sinha and Rishiraj Saha Roy) Short paper
11:50-12:10 A Hybrid Strategy for Privacy-Preserving Recommendations for Mobile Shopping (Toon De Pessemier, Kris Vanhecke and Luc Martens) Short paper
12:10-12:30 Morning discussion session
12:30-14:00 Lunch
14:00-14:30 Discovering Contextual Information from User Reviews for Recommendation Purposes (Konstantin Bauman and Alexander Tuzhilin) Long paper
14:30-15:00 A User-centered Music Recommendation Approach for Daily Activities (Ricardo Dias, Manuel J. Fonseca and Ricardo Cunha) Long paper
15:00-15:30 A Travel Recommender System for Combining Multiple Travel Regions to a Composite Trip (Daniel Herzog and Wolfgang Wörndl) Long paper
15:30-16:00 Coffee break
16:00-16:30 Exploiting Social Tags in Matrix Factorization Models for Cross-domain Collaborative Filtering (Ignacio Fernndez-Tobías and Iván Cantador) Long paper
16:30-17:00 Content-Based Cross-Domain Recommendations Using Segmented Models (Shaghayegh Sahebi and Trevor Walker, slides) Long paper
17:00-17:20 HybridRank: A Hybrid Content-Based Approach To Mobile Game Recommendations (Anthony Chow, Min-Hui Nicole Foo and Giuseppe Manai) Short paper
17:20-17:40 Afternoon discussion session

Long paper presentations are assigned 20 mins + 10 mins for discussion. Short papers are assigned 15 mins + 5 mins for discussion


Semantics-aware Content-based Recommender Systems by Pasquale Lops

Content-based recommender systems (CBRS) filter very large repositories of items (books, news, music tracks, TV assets, web pages...) by analyzing items previously rated by a user and building a model of user interests, called user profile, based on the features of the items rated by that user. The user profile is then exploited to recommend new potentially relevant items.

CBRS usually use textual features to represent items and user profiles, hence they inherit the classical problems of natural language ambiguity. The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of CBRS. Novel research works have introduced semantic techniques that shift a keyword-based representation of items and user profiles to a concept-based one.

In this talk I will focus on the main problems of CBRS, such as limited content analysis, and overspecialization, showing the current research directions for overcoming them, including:

  • top-down semantic approaches, based on the use of different open knowledge sources (ontologies, Wikipedia, DBpedia)
  • bottom-up semantic approaches, based on the distributional hypothesis, which states that "words that occur in the same contexts tend to have similar meanings"
  • cross-language recommender systems and algorithms for learning multilingual content-based profiles
  • the generation of serendipitous recommendations

Pasquale Lops is Assistant Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He received the Ph.D. in Computer Science from the University of Bari in 2005 with a dissertation on “Hybrid Recommendation Techniques based on User Profiles”. His research interests include recommender systems, machine learning, user modelling and information filtering. He has served as a senior program committee member of RecSys 2014 and as regular member since 2010.