recsysktl 2017


The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning (RecSysKTL) held in conjunction with the 11th ACM Conference on Recommender Systems, Como, Italy, 2017

Workshop Chairs

Yong Zheng, Illinois Institute of Technology, USA
Weike Pan, Shenzhen University, China
Shaghayegh (Sherry) Sahebi, University at Albany, SUNY, USA
Ignacio Fernández, NTENT, Barcelona, Spain

Program Committee Members

Alejandro Bellogín, Universidad Autónoma de Madrid
Steve Bourke, Schibsted Media Group
Iván Cantador, Universidad Autónoma de Madrid
Liang Dong, Google, Inc.
Mehdi Elahi, Free University of Bozen
Negar Hariri, Apple, Inc.
Mahesh Joshi, Linkedin
Bin Li, Data61, Australia
Zhongqi Lu, Hong Kong University of Science and Technology, Hong Kong, China
Cataldo Musto, University of Bari “Aldo Moro”
Denis Parra, Pontificia Universidad Catolica de Chile
Alan Said, University of Skövde, Sweden
Yue Shi, Facebook, USA
Fatemeh Vahedian, DePaul University
Saúl Vargas, Mendeley
Tong Yu, Carnegie Mellon University, USA
Fuzhen Zhuang, Chinese Academy of Sciences, China
Yong Zhuang, Carnegie Mellon University, USA

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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