| Vol-1887 urn:nbn:de:0074-1887-8 Copyright © 2017 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

# RecSysKTL 2017
Intelligent Recommender Systems by Knowledge Transfer & Learning

### Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning
co-located with ACM Conference on Recommender Systems (RecSys 2017)

### Como, Italy, August 27, 2017.

Edited by

### Yong Zheng (1)
Weike Pan (2)
Shaghayegh (Sherry) Sahebi (3)
Ignacio Fernández (4)

(1) Illinois Institute of Technology, USA
(2) Shenzhen University, China
(3) University at Albany, SUNY, USA
(4) NTENT, Barcelona, Spain

## 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

2017-08-06: submitted by Yong Zheng, metadata incl. bibliographic data published under Creative Commons CC0
2017-08-06: published on CEUR-WS.org |valid HTML5|