RecSysKTL 2017
Intelligent Recommender Systems by Knowledge Transfer & Learning

Table of Contents

• Preface
  
  

Long Papers

• Divide and Transfer: Understanding Latent Factors for Recommendation Tasks 1-8
  Vidyadhar Rao, Rosni K V, Vineet Padmanabhan
• Cross-Domain Recommendation for Large-Scale Data 9-15
  Shaghayegh Sahebi, Peter Brusilovsky, Vladimir Bobrokov
• Feature Factorization for Top-N Recommendation: From Item Rating to Features Relevance 16-21
  Vito Walter Anelli, Tommaso Di Noia, Pasquale Lops, Eugenio Di Sciascio
• Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits 22-29
  Anders Kolstad, Özlem Özgöbek, Jon Atle Gulla, Simon Litlehamar
• A Framework for Training Hybrid Recommender Systems 30-37
  Simon Bremer, Alan Schelten, Enrico Lohmann, Martin Kleinsteuber

Short Papers

• Transfer Learning from APP Domain to News Domain for Dual Cold-Start Recommendation 38-41
  Jixiong Liu, Jiakun Shi, Wanling Cai, Bo Liu, Weike Pan, Qiang Yang, Zhong Ming
• Tailoring Recommendations for a Multi-Domain Environment 42-45
  Emanuel Lacic, Dominik Kowald, Elisabeth Lex