Program


Note: There is a 25-minute presentation (QA included) for each paper

CEUR Proceeding is available at http://ceur-ws.org/Vol-1887/

Programs

8:10 – 8:20 Opening
8:25 – 8:50 Divide and Transfer: Understanding Latent Factors for Recommendation Tasks[Slide]
8:55 – 9:20 Cross-Domain Recommendation for Large-Scale Data[Slide]
9:25- 9:50 Transfer Learning from APP Domain to News Domain for Dual Cold-Start Recommendation[Slide]
9:55 – 10:20 Feature Factorization for Top-N Recommendation: From Item Rating to Features Relevance[Slide]
10:30 – 11:00 Break
11:00 – 11:25 A Framework for Training Hybrid Recommender Systems
11:30 – 11:55 Tailoring Recommendations for a Multi-Domain Environment[Slide]
12:00 – 12:25 Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits[Slide]
12:25 – 12:30 Closing Remarks
12:30 – 14:00 Lunch Break

Accepted papers

  • Rethinking Conventional Collaborative Filtering for Recommending Daily Fashion Outfits
    Anders Kolstad, Özlem Özgöbek, Jon Atle Gulla and Simon Litlehamar
  • Feature Factorization for top-n Recommendation: from item rating to features relevance
    Vito Walter Anelli, Tommaso Di Noia, Pasquale Lops and Eugenio Di Sciascio
  • A Framework for Training Hybrid Recommender Systems
    Simon Bremer, Alan Schelten, Enrico Lohmann and Martin Kleinsteuber
  • Cross-Domain Recommendation for Large-Scale Data
    Shaghayegh Sahebi, Peter Brusilovsky and Vladimir Bobrokov
  • Transfer Learning from APP Domain to News Domain for Dual Cold-Start Recommendation [Short paper]
    Jixiong Liu, Jiakun Shi, Wanling Cai, Bo Liu, Weike Pan, Qiang Yang and Zhong Ming
  • Tailoring Recommendations for a Multi-Domain Environment [Short paper]
    Emanuel Lacic, Dominik Kowald and Elisabeth Lex
  • Divide and Transfer: Understanding Latent Factors for Recommendation Tasks
    Vidyadhar Rao, Rosni K V and Vineet Padmanabhan
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