CBRecSys 2015
New Trends on Content-Based Recommender Systems

Table of Contents

• Preface
  
  
• Organization
  
  

• Keynote: Capturing User Interests for Content-based Recommendations 1
  Frank Hopfgartner
• Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity 2-9
  Paolo Tomeo, Tommaso Di Noia, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Eugenio Di Sciascio
• Automatic Selection of Linked Open Data Features in Graph-based Recommender Systems 10-13
  Cataldo Musto, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Simone Rutigliano
• Metadata Embeddings for User and Item Cold-start Recommendations 14-21
  Maciej Kula
• Generic knowledge-based Analysis of Social Media for Recommendations 22-29
  Victor de Graaff, Anne van de Venis, Maurice van Keulen, Rolf de By
• The Continuous Cold-start Problem in e-Commerce Recommender Systems 30-33
  Lucas Bernardi, Jaap Kamps, Julia Kiseleva, Melanie Mueller
• Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary 34-41
  Mickaël Poussevin, Vincent Guigue, Patrick Gallinari
• Cross-Document Search Engine For Book Recommendation 42-49
  Chahinez Benkoussas, Patrice Bellot
• Conceptual Impact-Based Recommender System for CiteSeerx 50-53
  Kevin Labille, Susan Gauch, Ann Smittu Joseph